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O-Bin: oblivious binning for encrypted data over cloud

conference contribution
posted on 2023-05-23, 11:02 authored by Ahmad, M, Pervez, Z, Byeong KangByeong Kang, Lee, S
In recent years, the data growth rate has been observed growing at a staggering rate. Considering data search as a primitive operation and to optimize this process on large volume of data, various solution have been evolved over a period of time. Other than finding the precise similarity, these algorithms aim to find the approximate similarities and arrange them into bins. Locality sensitive hashing (LSH) is one such algorithm that discovers probable similarities prior calculating the exact similarity thus enhance the overall search process in high dimensional search space. Realizing same strategy for encrypted data and that too in public cloud introduces few challenges to be resolved before probable similarity discovery. To address these issues and to formalize a similar strategy like LSH, in this paper we have formalized a technique O-Bin that is designed to work over encrypted data in cloud. By exploiting existing cryptographic primitives, O-Bin preserves the data privacy during the similarity discovery for the binning process. Our experimental evaluation for O-Bin produces results similar to LSH for encrypted data.


Publication title

Proceedings of the 29th International Conference on Advanced Information Networking and Applications


L Barolli, M Takizawa, F Xhafa, T Enokido, JH Park






School of Information and Communication Technology


IEEE-Inst Electrical Electronics Engineers Inc

Place of publication

New Jersey, USA

Event title

29th International Conference on Advanced Information Networking and Applications

Event Venue

Gwangju, Korea

Date of Event (Start Date)


Date of Event (End Date)


Rights statement

Copyright 2015 IEEE

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