With the increasing volume of images users share through social sites, maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users inadvertently shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. We examine the role of social context, image content, and metadata as possible indicators of users’ privacy preferences.
We propose a two-level
framework which according to the user’s available history on the site,
determines the best available privacy policy for the user’s images being
uploaded. Our solution relies on an image classification framework for image
categories which may be associated with similar policies, and on a policy
prediction algorithm to automatically generate a policy for each newly uploaded
image, also according to users’ social features. Over time, the generated
policies will follow the evolution of users’ privacy attitude. We provide the
results of our extensive evaluation over 5,000 policies, which demonstrate the
effectiveness of our system, with prediction accuracies over 90 percent.
1.2 INTRODUCTION
Images are now one of the key enablers of users’ connectivity. Sharing takes place both among previously established groups of known people or social circles (e. g., Google+, Flickr or Picasa), and also increasingly with people outside the users social circles, for purposes of social discovery-to help them identify new peers and learn about peers interests and social surroundings. However, semantically rich images may reveal contentsensitive information. Consider a photo of a students 2012 graduationceremony, for example.
It could be shared within a Google+ circle or Flickr group, but may unnecessarily expose the studentsBApos familymembers and other friends. Sharing images within online content sharing sites,therefore,may quickly leadto unwanted disclosure and privacy violations. Further, the persistent nature of online media makes it possible for other users to collect rich aggregated information about the owner of the published content and the subjects in the published content. The aggregated information can result in unexpected exposure of one’s social environment and lead to abuse of one’s personal information.
Most content sharing websites allow users to enter
their privacy preferences. Unfortunately, recent studies have shown that users
struggle to set up and maintain such privacy settings. One of the main reasons
provided is that given the amount of shared information this process can be
tedious and error-prone. Therefore, many have acknowledged the need of policy
recommendation systems which can assist users to easily and properly configure
privacy settings. However, existing proposals for automating privacy settings
appear to be inadequate to address the unique privacy needs of images due to the
amount of information implicitly carried within images, and their relationship
with the online environment wherein they are exposed.
1.3 LITRATURE SURVEY
TITLE NAME: SHEEPDOG: GROUP AND TAG RECOMMENDATION FOR FLICKR PHOTOS BY AUTOMATIC SEARCH-BASED LEARNING
AUTHOR: H.-M. Chen, M.-H. Chang, P.-C. Chang, M.-C. Tien, W. H. Hsu, and J.-L. Wu,
PUBLISH: Proc. 16th ACM Int. Conf. Multimedia, 2008, pp. 737–740.
EXPLANATION:
Online photo albums have been prevalent in recent
years and have resulted in more and more applications developed to provide
convenient functionalities for photo sharing. In this paper, we propose a
system named SheepDog to automatically add photos into appropriate groups and
recommend suitable tags for users on Flickr. We adopt concept detection to
predict relevant concepts of a photo and probe into the issue about training
data collection for concept classification. From the perspective of gathering
training data by web searching, we introduce two mechanisms and investigate
their performances of concept detection. Based on some existing information
from Flickr, a ranking-based method is applied not only to obtain reliable
training data, but also to provide reasonable group/tag recommendations for
input photos. We evaluate this system with a rich set of photos and the results
demonstrate the effectiveness of our work.
TITLE NAME: CONNECTING CONTENT TO COMMUNITY IN SOCIAL MEDIA VIA IMAGE CONTENT, USER TAGS AND USER COMMUNICATION
AUTHOR: M. D. Choudhury, H. Sundaram, Y.-R. Lin, A. John, and D. D. Seligmann
PUBLISH: Proc. IEEE Int. Conf. Multimedia Expo, 2009, pp.1238–1241.
EXPLANATION:
In this paper we develop a
recommendation framework to connect image content with communities in online
social media. The problem is important because users are looking for useful
feedback on their uploaded content, but finding the right community for
feedback is challenging for the end user. Social media are characterized by
both content and community. Hence, in our approach, we characterize images through
three types of features: visual features, user generated text tags, and social
interaction (user communication history in the form of comments). A
recommendation framework based on learning a latent space representation of the
groups is developed to recommend the most likely groups for a given image. The
model was tested on a large corpus of Flickr images comprising 15,689 images.
Our method outperforms the baseline method, with a mean precision 0.62 and mean
recall 0.69. Importantly, we show that fusing image content, text tags with
social interaction features outperforms the case of only using image content or
tags.
TITLE NAME: ANALYSING FACEBOOK FEATURES TO SUPPORT EVENT DETECTION FOR PHOTO-BASED FACEBOOK APPLICATIONS
AUTHOR: M. Rabbath, P. Sandhaus, and S. Boll,
PUBLISH: Proc. 2nd ACM Int. Conf. Multimedia Retrieval, 2012, pp. 11:1–11:8.
EXPLANATION:
Facebook witnesses an explosion of
the number of shared photos: With 100 million photo uploads a day it creates as
much as a whole Flickr each two months in terms of volume. Facebook has also
one of the healthiest platforms to support third party applications, many of
which deal with photos and related events. While it is essential for many
Facebook applications, until now there is no easy way to detect and link photos
that are related to the same events, which are usually distributed between
friends and albums. In this work, we introduce an approach that exploits
Facebook features to link photos related to the same event. In the current situation
where the EXIF header of photos is missing in Facebook, we extract
visual-based, tagged areas-based, friendship-based and structure-based
features. We evaluate each of these features and use the results in our
approach. We introduce and evaluate a semi-supervised probabilistic approach
that takes into account the evaluation of these features. In this approach we
create a lookup table of the initialization values of our model variables and
make it available for other Facebook applications or researchers to use. The
evaluation of our approach showed promising results and it outperformed the
other the baseline method of using the unsupervised EM algorithm in estimating
the parameters of a Gaussian mixture model. We also give two examples of the
applicability of this approach to help Facebook applications in better serving
the user.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Image content sharing environments such as Flickr or YouTube contain a large amount of private resources such as photos showing weddings, family holidays, and private parties. These resources can be of a highly sensitive nature, disclosing many details of the users’ private sphere. In order to support users in making privacy decisions in the context of image sharing and to provide them with a better overview on privacy related visual content available on the Web techniques to automatically detect private images, and to enable privacy-oriented image search.
To this end, we learn privacy classifiers trained on a large set of manually assessed Flickr photos, combining textual metadata of images with a variety of visual features. We employ the resulting classification models for specifically searching for private photos, and for diversifying query results to provide users with a better coverage of private and public content. Most content sharing websites allow users to enter their privacy preferences. Unfortunately, recent studies have shown that users struggle to set up and maintain such privacy settings.
- One of the main reasons provided is that given the amount of shared information this process can be tedious and error-prone of policy recommendation systems which can assist users too easily and properly configure privacy settings.
2.1.1 DISADVANTAGES:
- Sharing images within online content sharing sites, therefore, may quickly lead to unwanted disclosure and privacy violations.
- Further, the persistent nature of online media makes it possible for other users to collect rich aggregated information about the owner of the published content and the subjects in the published content.
- The aggregated information can result in unexpected exposure of one’s social environment and lead to abuse of one’s personal information.
2.2 PROPOSED SYSTEM:
We propose an Adaptive Privacy Policy Prediction (A3P) system which aims to provide users a hassle free privacy settings experience by automatically generating personalized policies. The A3P system handles user uploaded images, and factors in the following criteria that influence one’s privacy settings of images:
The impact of social environment and personal characteristics: Social context of users, such as their profile information and relationships with others may provide useful information regarding users’ privacy preferences. For example, users interested in photography may like to share their photos with other amateur photographers. Users who have several family members among their social contacts may share with them pictures related to family events. However, using common policies across all users or across users with similar traits may be too simplistic and not satisfy individual preferences.
Users may have drastically different opinions even on the same type of images. For example, a privacy adverse person may be willing to share all his personal images while a more conservative person may just want to share personal images with his family members. In light of these considerations, it is important to find the balancing point between the impact of social environment and users’ individual characteristics in order to predict the policies that match each individual’s needs.
The role of image’s content and metadata: In general, similar images often incur similar privacy preferences, especially when people appear in the images. For example, one may upload several photos of his kids and specify that only his family members are allowed to see these photos. He may upload some other photos of landscapes which he took as a hobby and for these photos, he may set privacy preference allowing anyone to view and comment the photos. Analyzing the visual content may not be sufficient to capture users’ privacy preferences. Tags and other metadata are indicative of the social context of the image, including where it was taken and why, and also provide a synthetic description of images, complementing the information obtained from visual content analysis.
2.2.1 ADVANTAGES:
- The A3P-core focuses on analyzing each individual user’s own images and metadata, while the A3P-Social offers a community perspective of privacy setting recommendations for a user’s potential privacy improvement.
- Our algorithm in A3P-core (that is now parameterized based on user groups and also factors in possible outliers), and a new A3P-social module that develops the notion of social context to refine and extend the prediction power of our system.
- We design the interaction flows between the two building blocks to balance the benefits from meeting personal characteristics and obtaining community advice.
2.3 HARDWARE & SOFTWARE REQUIREMENTS:
2.3.1 HARDWARE REQUIREMENT:
v Processor – Pentium –IV
- Speed –
1.1 GHz
- RAM – 256 MB (min)
- Hard Disk – 20 GB
- Floppy Drive – 1.44 MB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA
2.3.2 SOFTWARE REQUIREMENTS:
- Operating System : Windows XP or Win7
- Front End : JAVA JDK 1.7
- Back End : MYSQL Server
- Server : Apache Tomact Server
- Script : JSP Script
- Document : MS-Office 2007
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
- The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of the input data to the system, various processing carried out on these data, and the output data is generated by the system
- The data flow diagram (DFD) is one of the most important modeling tools. It is used to model the system components. These components are the system process, the data used by the process, an external entity that interacts with the system and the information flows in the system.
- DFD shows how the information moves through the system and how it is modified by a series of transformations. It is a graphical technique that depicts information flow and the transformations that are applied as data moves from input to output.
- DFD is also known as bubble chart. A DFD may be used to represent a system at any level of abstraction. DFD may be partitioned into levels that represent increasing information flow and functional detail.
NOTATION:
SOURCE OR DESTINATION OF DATA:
External sources or destinations, which may be people or organizations or other entities
DATA SOURCE:
Here the data referenced by a process is stored and retrieved.
PROCESS:
People, procedures or devices that produce data’s in the physical component is not identified.
DATA FLOW:
Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.
There are several common modeling rules when creating DFDs:
- All processes must have at least one data flow in and one data flow out.
- All processes should modify the incoming data, producing new forms of outgoing data.
- Each data store must be involved with at least one data flow.
- Each external entity must be involved with at least one data flow.
- A data flow must be attached to at least one process.
3.1 ARCHITECTURE DIAGRAM
3.2 DATAFLOW DIAGRAM
ADMIN:
USER:
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
ADMIN:
USER:
3.3 CLASS DIAGRAM:
ADMIN:
USER:
3.4 SEQUENCE DIAGRAM:
ADMIN:
USER:
3.5 ACTIVITY DIAGRAM:
ADMIN:
USER:
CHAPTER 4
4.0 IMPLEMENTATION:
A3P-CORE
There are two major components in A3P-core: (i) Image classification and (ii) Adaptive policy prediction. For each user, his/her images are first classified based on content and metadata. Then, privacy policies of each category of images are analyzed for the policy prediction. Adopting a two-stage approach is more suitable for policy recommendation than applying the common one-stage data mining approaches to mine both image features and policies together. Recall that when a user uploads a new image, the user is waiting for a recommended policy.
The two-stage approach allows the system to employ
the first stage to classify the new image and find the candidate sets of images
for the subsequent policy recommendation. As for the one-stage mining approach,
it would not be able to locate the right class of the new image because its
classification criteria need both image features and policies whereas the
policies of the new image are not available yet. Moreover, combining both image
features and policies into a single classifier would lead to a system which is
very dependent to the specific syntax of the policy. If a change in the
supported policies were to be introduced, the whole learning model would need
to change.
A3P-SOCIAL
The A3P-social employs a multi-criteria inference mechanism that generates representative policies by leveraging key information related to the user’s social context and his general attitude toward privacy. As mentioned earlier, A3Psocial will be invoked by the A3P-core in two scenarios. One is when the user is a newbie of a site, and does not have enough images stored for the A3P-core to infer meaningful and customized policies. The other is when the system notices significant changes of privacy trend in theuser’s social circle, which may be of interest for the user to possibly adjust his/her privacy settings accordingly. In what follows, we first present the types of social context considered by A3P-Social, and then present the policy recommendation process.
4.1 ALGORITHM
Our algorithm performs better for users with certain characteristics. Therefore, we study possible factors relevant to the performance of our algorithm. We used a least squares multiple regression analysis, regressing performance of the A3P-core to the following possible predictors:
4.2 MODULES:
WEB-BASED IMAGE SHARING SERVICES:
METADATA-BASED CLASSIFICATION:
CONTENT-BASED CLASSIFICATION:
ADAPTIVE
POLICY PREDICTION:
4.3 MODULE DESCRIPTION:
WEB-BASED IMAGE SHARING SERVICES:
Sharing images within online content sharing sites, therefore, may quickly lead to unwanted disclosure and privacy violations. Further, the persistent nature of online media makes it possible for other users to collect rich aggregated information about the owner of the published content and the subjects in the published content. The aggregated information can result in unexpected exposure of one’s social environment and lead to abuse of one’s personal information. We expected that frequency of sharing pictures and frequency of changing privacy settings would be significantly related to performance, but the results indicate that the frequency of social network use, frequency of uploading images and frequency of changing settings are not related to the performance our algorithm obtains with privacy settings predictions. This is a particularly useful result as it indicates that our algorithm will perform equally well for users who frequently use and share images on social networks as well as for users who may have limited access or limited information to share.
METADATA-BASED CLASSIFICATION:
We propose a hierarchical image
classification which classifies images first based on their contents and then
refine each category into subcategories based on their metadata. Images that do
not have metadata will be grouped only by content. Such a hierarchical
classification gives a higher priority to image content and minimizes the
influence of missing tags. Note that it is possible that some images are
included in multiple categories as long as they contain the typical content
features or metadata based classification groups’ images into subcategories
under aforementioned baseline categories.
The process consists of three main steps.
The third step is to find a subcategory that an image belongs to. This is an incremental procedure. At the beginning, the first image forms a subcategory as itself and the representative hypernyms of the image becomes the subcategory’s representative hypernyms. Then, we compute the distance between representative hypernyms of a new incoming image and each existing subcategory.
CONTENT-BASED CLASSIFICATION:
Our approach to content-based classification is based on an efficient and yet accurate image similarity approach. Specifically, our classification algorithm compares image signatures defined based on quantified and sanitized version of Haar wavelet transformation. For each image, the wavelet transform encodes frequency and spatial information related to image color, size, invariant transform, shape, texture, symmetry, etc. Then, a small number of coefficients are selected to form the signature of the image. The content similarity among images is then determined by the distance among their image signatures.
Our selected similarity criteria include texture, symmetry, shape (radial symmetry and phase congruency and SIFT. We also account for color and size. We set the system to start from five generic image classes: (a) explicit (e.g., nudity, violence, drinking etc), (b) adults, (c) kids, (d) scenery (e.g., beach, mountains), (e) animals. As a preprocessing step, we populate the five baseline classes by manually assigning to each class a number of images crawled from Google images, resulting in about 1,000 images per class. Having a large image data set beforehand reduces the chance of misclassification. Then, we generate signatures of all the images and store them in the database.
Our content classifier, we conducted some preliminary test to evaluate its accuracy. Precisely, we tested our classifier it against a ground-truth data set, Image-net.org. In Image-net, over 10 million images are collected and classified according to the wordnet structure. For each image class, we use the first half set of images as the training data set and classify the next 800 images. The classification result was recorded as correct if the synset’s main search term or the direct hypernym is returned as a class. The average accuracy of our classifier is above 94 percent.
ADAPTIVE POLICY PREDICTION:
The policy prediction algorithm provides a predicted policy of a newly uploaded image to the user for his/her reference. More importantly, the predicted policy will reflect the possible changes of a user’s privacy concerns. The prediction process consists of three main phases: (i) policy normalization; (ii) policy mining; and (iii) policy prediction. The policy normalization is a simple decomposition process to convert a user policy into a set of atomic rules in which the data (D) component is a single-element set.
We propose a hierarchical mining approach for policy mining. Our approach leverages association rule mining techniques to discover popular patterns in policies. Policy mining is carried out within the same category of the new image because images in the same category are more likely under the similar level of privacy protection. The basic idea of the hierarchical mining is to follow a natural order in which a user defines a policy.
Given an image, a user usually first
decides who can access the image, then thinks about what specific access rights
(e.g., view only or download) should be given, and finally refine the access
conditions such as setting the expiration date. Correspondingly, the
hierarchical mining first look for popular subjects defined by the user, then
look for popular actions in the policies containing the popular subjects, and
finally for popular conditions in the policies containing both popular subjects
and conditions.
CHAPTER 5
5.0 SYSTEM STUDY:
5.1 FEASIBILITY STUDY:
The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.
Three key considerations involved in the feasibility analysis are
- ECONOMICAL FEASIBILITY
- TECHNICAL FEASIBILITY
- SOCIAL FEASIBILITY
5.1.1 ECONOMICAL FEASIBILITY:
This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.
5.1.2 TECHNICAL FEASIBILITY
This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.
5.1.3 SOCIAL FEASIBILITY:
The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.
5.2 SYSTEM TESTING:
Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months.
This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.
5.2.1 UNIT TESTING:
| Description | Expected result |
| Test for application window properties. | All the properties of the windows are to be properly aligned and displayed. |
| Test for mouse operations. | All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions. |
A program represents the
logical elements of a system. For a program to run satisfactorily, it must
compile and test data correctly and tie in properly with other programs.
Achieving an error free program is the responsibility of the programmer.
Program testing checks
for two types
of errors: syntax
and logical. Syntax error is a
program statement that violates one or more rules of the language in which it
is written. An improperly defined field dimension or omitted keywords are
common syntax errors. These errors are shown through error message generated by
the computer. For Logic errors the programmer must examine the output
carefully.
5.1.2 FUNCTIONAL TESTING:
Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem. The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.
| Description | Expected result |
| Test for all modules. | All peers should communicate in the group. |
| Test for various peer in a distributed network framework as it display all users available in the group. | The result after execution should give the accurate result. |
5.1. 3 NON-FUNCTIONAL TESTING:
The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:
- Load testing
- Performance testing
- Usability testing
- Reliability testing
- Security testing
5.1.4 LOAD TESTING:
An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.
| Description | Expected result |
| It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received. | Should designate another active node as a Server. |
5.1.5 PERFORMANCE TESTING:
Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.
| Description | Expected result |
| This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management. | Should handle large input values, and produce accurate result in a expected time. |
5.1.6 RELIABILITY TESTING:
The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.
| Description | Expected result |
| This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application. | In case of failure of the server an alternate server should take over the job. |
5.1.7 SECURITY TESTING:
Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.
| Description | Expected result |
| Checking that the user identification is authenticated. | In case failure it should not be connected in the framework. |
| Check whether group keys in a tree are shared by all peers. | The peers should know group key in the same group. |
5.1.8 WHITE BOX TESTING:
White box testing, sometimes called glass-box testing is a test case design method that uses the control structure of the procedural design to derive test cases. Using white box testing method, the software engineer can derive test cases. The White box testing focuses on the inner structure of the software structure to be tested.
| Description | Expected result |
| Exercise all logical decisions on their true and false sides. | All the logical decisions must be valid. |
| Execute all loops at their boundaries and within their operational bounds. | All the loops must be finite. |
| Exercise internal data structures to ensure their validity. | All the data structures must be valid. |
5.1.9 BLACK BOX TESTING:
Black box testing, also called behavioral testing, focuses on the functional requirements of the software. That is, black testing enables the software engineer to derive sets of input conditions that will fully exercise all functional requirements for a program. Black box testing is not alternative to white box techniques. Rather it is a complementary approach that is likely to uncover a different class of errors than white box methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.
| Description | Expected result |
| To check for incorrect or missing functions. | All the functions must be valid. |
| To check for interface errors. | The entire interface must function normally. |
| To check for errors in a data structures or external data base access. | The database updation and retrieval must be done. |
| To check for initialization and termination errors. | All the functions and data structures must be initialized properly and terminated normally. |
All
the above system testing strategies are carried out in as the development,
documentation and institutionalization of the proposed goals and related
policies is essential.
CHAPTER 6
6.0 SOFTWARE DESCRIPTION:
6.1 JAVA TECHNOLOGY:
Java technology is both a programming language and a platform.
The Java Programming Language
The Java programming language is a high-level language that can be characterized by all of the following buzzwords:
- Simple
- Architecture neutral
- Object oriented
- Portable
- Distributed
- High performance
- Interpreted
- Multithreaded
- Robust
- Dynamic
- Secure
With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.
You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.
6.2 THE JAVA PLATFORM:
A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.
The Java platform has two components:
- The Java Virtual Machine (Java VM)
- The Java Application Programming Interface (Java API)
You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.
The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.
The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.
Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.
6.3 WHAT CAN JAVA TECHNOLOGY DO?
The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.
However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.
An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.
A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.
How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:
- The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.
- Applets: The set of conventions used by applets.
- Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.
- Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.
- Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.
- Software components: Known as JavaBeansTM, can plug into existing component architectures.
- Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).
- Java Database Connectivity (JDBCTM): Provides uniform access to a wide range of relational databases.
The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.
6.4 HOW WILL JAVA TECHNOLOGY CHANGE MY LIFE?
We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:
- Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.
- Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.
- Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wide-ranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.
- Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.
- Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure JavaTM Product Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.
- Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.
- Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.
6.5 ODBC:
Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.
Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.
The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.
From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.
The advantages
of this scheme are so numerous that you are probably thinking there must be
some catch. The only disadvantage of ODBC is that it isn’t as efficient as
talking directly to the native database interface. ODBC has had many detractors
make the charge that it is too slow. Microsoft has always claimed that the
critical factor in performance is the quality of the driver software that is
used. In our humble opinion, this is true. The availability of good ODBC
drivers has improved a great deal recently. And anyway, the criticism about
performance is somewhat analogous to those who said that compilers would never
match the speed of pure assembly language. Maybe not, but the compiler (or
ODBC) gives you the opportunity to write cleaner programs, which means you
finish sooner. Meanwhile, computers get faster every year.
6.6 JDBC:
In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.
To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.
JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.
The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.
6.7 JDBC Goals:
Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.
The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:
SQL Level API
The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.
SQL Conformance
SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.
JDBC must be implemental on top of common database interfaces
The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.
- Provide a Java interface that is consistent with the rest of the Java system
Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.
- Keep it simple
This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.
- Use strong, static typing wherever possible
Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.
- Keep the common cases simple
Because more often than not, the usual SQL calls
used by the programmer are simple SELECT’s,
INSERT’s,
DELETE’s
and UPDATE’s,
these queries should be simple to perform with JDBC. However, more complex SQL
statements should also be possible.
Finally we decided to precede the implementation using Java Networking.
And for dynamically updating the cache table we go for MS Access database.
Java ha two things: a programming language and a platform.
Java is a high-level programming language that is all of the following
Simple Architecture-neutral
Object-oriented Portable
Distributed High-performance
Interpreted Multithreaded
Robust Dynamic Secure
Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.
Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.
6.7 NETWORKING TCP/IP STACK:
The TCP/IP stack is shorter than the OSI one:
TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.
IP datagram’s:
The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.
UDP:
UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.
TCP:
TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.
Internet addresses
In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.
Network address:
Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.
Subnet address:
Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.
Host address:
8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.
Total address:
The 32 bit address is usually written as 4 integers separated by dots.
Port addresses
A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.
Sockets:
A socket is a data structure maintained by the system
to handle network connections. A socket is created using the call socket. It returns an integer that is like a file
descriptor. In fact, under Windows, this handle can be used with Read File and Write File
functions.
#include <sys/types.h>
#include <sys/socket.h>
int socket(int family, int type, int protocol);
Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two
processes wishing to communicate over a network create a socket each. These are
similar to two ends of a pipe – but the actual pipe does not yet exist.
6.8 JFREE CHART:
JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:
A consistent and well-documented API, supporting a wide range of chart types;
A flexible design that is easy to extend, and targets both server-side and client-side applications;
Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);
JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.
6.8.1. Map Visualizations:
Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);
Creating an appropriate dataset interface (plus
default implementation), a rendered, and integrating this with the existing
XYPlot class in JFreeChart; Testing, documenting, testing some more,
documenting some more.
6.8.2. Time Series Chart Interactivity
Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.
6.8.3. Dashboards
There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.
6.8.4. Property Editors
The property editor mechanism in JFreeChart only
handles a small subset of the properties that can be set for charts. Extend (or
reimplement) this mechanism to provide greater end-user control over the
appearance of the charts.
CHAPTER 7
7.0 APPENDIX
7.1 SAMPLE SCREEN SHOTS:
7.2
SAMPLE SOURCE CODE:
CHAPTER 8
8.1 CONCLUSION
A3P-Social, we achieve a much higher accuracy, demonstrating that just simply considering privacy inclination is not enough, and that ”social-context” truly matters. Precisely the overall accuracy of A3P-social is above 95 percent. For 88.6 percent of the users, all predicted policies are correct, and the number of missed policies is 33 (for over 2,600 predictions). Also, we note that in this case, there is no significant difference across image types.
We compared the performance of the A3P-Social with alternative, popular, recommendation methods: Cosine similarity is a measure of similarity between two vectors of an inner product space that measures the cosine of the angle between them. In our case, the vectors are the users’ attributes defining their social profile. The algorithm using Cosine similarity scans all users profiles, computes Cosine similarity of the social contexts between the new user and the existing users. Then, it finds the top two users with the highest similarity score with the candidate user and feeds the associated images to the remaining functions in the A3P-core.
We have proposed an Adaptive Privacy
Policy Prediction (A3P) system that helps users automate the privacy policy
settings for their uploaded images. The A3P system provides a comprehensive
framework to infer privacy preferences based on the information available for a
given user. We also effectively tackled the issue of cold-start, leveraging
social context information. Our experimental study proves that our A3P is a practical
tool that offers significant improvements over current approaches to privacy.