Joint NFF: Brown University & University of Maryland

Roberto Tamassia   

Babis Papamanthou      

Secure Deduplication and Compression for Big Data 

Hybrid clouds are increasingly being deployed and enable seamless data movement between public and private environments. However, when data is stored in a public cloud, significant challenges arise in making encryption techniques work together with search, deduplication, and compression. The proposed project has two main components: (1) deduplication of encrypted data and (2) searching compressed and encrypted data. Concerning deduplication, they propose a new technique for deduplicating encrypted data that is based on locality-sensitive hashing and tolerates small changes in the underlying plaintext data without blowing up the space too much. Concerning search, they propose using new compression techniques inspired by the database community to perform searches on encrypted data much more efficiently that existing systems. They expect their research to improve the state-of-the-art both in theory and systems and to even lead to new approaches and algorithms for performing more efficient queries on deduplicated and compressed unencrypted data.