2014 IEEE JAVA NETWORK SECURITY PROJECT Profilr toward preserving privacy and functionality in geosocial networks
- 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com PROFILR : Toward Preserving Privacy and Functionality in Geosocial Networks ABSTRACT: Profit is the main participation incentive for social network providers. Its reliance on user profiles, built from a wealth of voluntarily revealed personal information, exposes users to a variety of privacy vulnerabilities. In this paper, we propose to take first steps toward addressing the conflict between profit and privacy in geosocial networks. We introduce PROFILR, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations (i.e., venues). P ROFIL R endows users with strong privacy guarantees and providers with correctness assurances. In addition to a venue centric approach, we propose a decentralized solution for computing real time LCP snapshots over the profiles of colocated users. An Android implementation shows that P ROFIL R is efficient; the end-to-end overhead is small even under strong privacy and correctness assurances.
- 2. EXISTING SYSTEM: Online social networks have become a significant source of personal information. Their users voluntarily reveal a wealth of personal data, including age, gender, contact information, preferences and status updates. A recent addition to this space, geosocial networks (GSNs) such as Yelp and Foursquare further collect fine grained location information, through check-ins performed by users at visited venues. Overtly, personal information allows GSN providers to offer a variety of applications, including personalized recommendations and targeted advertising, and venue owners to promote their businesses through spatio-temporal incentives, e.g., rewarding frequent customers through accumulated badges DISADVANTAGES OF EXISTING SYSTEM: Providing personal information exposes however users to significant risks. As social networks have been shown to leak and even sell user data to third parties. PROPOSED SYSTEM: First, we propose a venue centric P ROFIL R , that relieves the GSN provider from a costly involvement in venue specific activities. To achieve this, P ROFIL R stores and builds LCPs at venues. Furthermore, it relies on Benaloh’s homomorphic cryptosystem and zero knowledge proofs to enable oblivious and
- 3. provable correct LCP computations. We prove that P ROFIL R satisfies the introduced correctness and privacy properties. Second, we propose a completely decentralized PROFIL R extension, built around the notion of snapshot LCPs. The distributed P ROFIL R enables user devices to aggregate the profiles of co-located users, without assistance from a venue device. Snapshot LCPs are not bound to venues, but instead user devices can compute LCPs of neighbors at any location of interest. Communications in both PROFILR implementations are performed over ad hoc wireless connections. ADVANTAGES OF PROPOSED SYSTEM: Privacy preserving, personalized public safety recommendations and Privately building real time statistics over the profiles of venue patrons with Yelp accounts. Evaluate PROFIL R through an Android implementation. Show that PROFILR is efficient even when deployed on previous generation Smartphones. SYSTEM ARCHITECTURE:
- 4. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS: System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Floppy Drive : 1.44 Mb. Monitor : 15 VGA Colour. Mouse : Logitech. Ram : 512 Mb. SOFTWARE REQUIREMENTS:
- 5. Operating system : Windows XP/7. Coding Language : JAVA/J2EE IDE : Netbeans 7.4 Database : MYSQL REFERENCE: Bogdan Carbunar, Mahmudur Rahman, Jaime Ballesteros, Naphtali Rishe, and Athanasios V. Vasilakos “PROFILR : Toward Preserving Privacy and Functionality in Geosocial Networks” IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 9, NO. 4, APRIL 2014.