Sunday 18 October 2015

Privacy Policy Inference of User-Uploaded Images on Content Sharing Sites


Abstract
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.
Aim
The main aim is to propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for the images shared through social sites.
Scope
The scope of the project is 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.
Existing System
There is a large body of work on image content analysis, for classification and interpretation retrieval and photo ranking also in the context of online photo sharing sites. Of these works, existing work is probably the closest to ours. The existing system explores privacy-aware image classification using a mixed set of features, both content and meta-data.
Disadvantages
·      This is however a binary classification (private versus public), so the classification task is very different than ours.
·      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.
·      Users struggle to set up and maintain privacy settings in the most content sharing websites.
Proposed System
Proposed system is, 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.
·      The role of image’s content and metadata.
Advantages
The A3P system provides a comprehensive framework to infer privacy preferences based on the information available for a given user. A3P also effectively tackled the issue of cold-start, leveraging social context information. This project proves that our A3P is a practical tool that offers significant improvements over current approaches to privacy.
System Architecture


System Configuration


Hardware Requirements
  • Speed                  -    1.1 Ghz
  • Processor              -    Pentium IV
  • RAM                    -    512 MB (min)
  • Hard Disk            -    40 GB
  • Key Board                    -    Standard Windows Keyboard
  • Mouse                  -    Two or Three Button Mouse
  • Monitor                -     LCD/LED
 Software requirements
  • Operating System              : Windows 7             
  •  Front End                           : ASP.Net and C#
  • Database                             : MSSQL
  • Tool                                    : Microsoft Visual studio
References
Dan Lin, Sundareswaran, S., Wede, J.PRIVACY POLICY INFERENCE OF USER-UPLOADED IMAGES ON CONTENT SHARING SITES”  Knowledge and Data Engineering, IEEE Transactions on  (Volume:27 ,  Issue: 1 ) April 2014

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