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Tuesday, January 24 • 4:30pm - 6:00pm
Poster: Sub-linear Privacy-preserving Search on Sensitive Dataset

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Privacy-preserving Near-neighbor search (PP-NNS) is a well- studied problem in the literature. The overwhelming growth in the size of current datasets and the lack of any truly secure server in the online world render the existing solutions impractical either due to their high computational requirements or the non-realistic assumptions which potentially compromise privacy. PP-NNS with multiple (semi-honest) data owners having query time sub-linear in the number of users has been proposed as an open research direction. We provide the first such algorithm which has a sub-linear query time and the ability to handle semi-honest (honest but curious) parties. Our algorithm can further manage the situation where a large chunk of the server information is being compromised.

Tuesday January 24, 2017 4:30pm - 6:00pm
BioScience Research Collaborative Event Hall 6500 Main Street, Houston, TX 77030-1402

Attendees (1)