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Past Projects.

Secure and Private Distributed Collaborative Intrusion Detection.

 

Overview: Sharing data among organizations is essential in detecting attacks in computer networks. When this collaboration is done in the sectoral or international levels, there is no trusted entity, and the data analysis has to be done in a distributed manner. For organizations to agree to share the data, it should be ensured that no confidential data will be leaked. In addition, this data contains in many cases information of individual clients. For legal reasons and to retain clients’ trust, it is importantthat information on individuals doesn’t leak. To prevent such leakage, we want to provide differential privacy, which, informally, guarantees no information leakage. (Read More)

 


 

Feasibility, Infeasibility, and Applications to Differential Privacy.

 

Overview: In today’s Internet, parties communicate and perform common tasks together, where each party holds some private information; however, parties cannot be trusted and some of them are malicious. Secure multiparty computation ensures that tasks can be performed securely in untrusted environments, and it can be used to model almost any cryptographic task, including coin-tossing, electronic voting, electronic auctions, and distributed private data analysis. It is known that, assuming that trap-door permutations exist, any function can be computed with full security whenever the honest parties form the majority of the participating parties. (Read More)

 


 

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