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Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation
Abstract
Personalized recommendation is crucial to help users find pertinent information. It often relies on a large collection of user data, in particular users’ online activity (e.g., tagging/rating/checking-in) on social media, to mine user preference. However, releasing such user activity data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from the users’ activity data. In this paper, we proposed PrivRank, a customizable and continuous privacy-preserving social media data publishing framework protecting users against inference attacks while enabling personalized ranking-based recommendations. Its key idea is to continuously obfuscate user activity data such that the privacy leakage of user-specified private data is minimized under a given data distortion budget, which bounds the ranking loss incurred from the data obfuscation process in order to preserve the utility of the data for enabling recommendations.
Existing System
To protect user privacy when publishing user data, the current practice mainly relies on policies or user agreements, e.g., on the use and storage of the published data. However, this approach cannot guarantee that the users’ sensitive information is actually protected from a malicious attacker. Therefore, to provide effective privacy protection when releasing user data, privacy-preserving data publishing has been widely studied. Its key idea is to obfuscate user data such that published data remains useful for some application scenarios while the individual’s privacy is preserved. According to the attacks considered, existing work can be classified into two categories. The first category is based on heuristic techniques to protect ad-hoc defined user privacy. Specific solutions mainly tackle the privacy threat when attackers are able to link the data owner’s identity to a record, or an attribute in the published data. The second category is theory-based and focuses on the uninformative principle, i.e., on the fact that the published data should provide attackers with as little additional information as possible beyond background knowledge.
Disadvantages
Ø Privacy is less.
Ø Performance is low.
Proposed System
The system proposes PrivRank, a customizable and continuous privacy preserving data publishing framework protect users against inference attacks while enabling personalized ranking based recommendation. It provides continuous protection of user-specified private data against inference attacks by obfuscating both the historical and streaming user activity data before releasing them, while still preserving the utility of the published data for enabling personalized ranking based recommendation by efficiently limiting the pair wise ranking loss incurred from data obfuscation.
Advantages
Ø Privacy is more.
Ø Performance is better.
SYSTEM REQUIREMENTS
➢ H/W System Configuration:-
➢ Processor - Pentium –IV
➢ RAM - 4 GB (min)
➢ Hard Disk - 20 GB
➢ Key Board - Standard Windows Keyboard
➢ Mouse - Two or Three Button Mouse
➢ Monitor - SVGA
SOFTWARE REQUIREMENTS:
v Operating system : Windows 7 Ultimate.
v Coding Language : Python.
v Front-End : Python.
v Back-End : Django-ORM
v Designing : Html, css, javascript.
v Data Base : MySQL (WAMP Server).
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