Anrin Chakraborti (Stony Brook University), Radu Sion (Stony Brook University)

ConcurORAM is a parallel, multi-client oblivious RAM (ORAM) that eliminates waiting for concurrent stateless clients and allows over-all throughput to scale gracefully, without requiring trusted third party components (proxies) or direct inter-client coordination. A key insight behind ConcurORAM is the fact that, during multi-client data access, only a subset of the concurrently-accessed server-hosted data structures require access privacy guarantees. Everything else can be safely implemented as oblivious data structures that are later synced securely and efficiently during an ORAM “eviction”.

Further, since a major contributor to latency is the eviction– in which client-resident data is reshuffled and reinserted back encrypted into the main server database – ConcurORAM also enables multiple concurrent clients to evict asynchronously, in parallel (without compromising consistency), and in the back-ground without having to block ongoing queries. As a result, throughput scales well with increasing number of concurrent clients and is not significantly impacted by evictions. For example, about 65 queries per second can be executed in parallel by 30 concurrent clients, a 2x speedup over the state-of-the-art. The query access time for individual clients increases by only 2x when compared to a single-client deployment.

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Cornelius Aschermann (Ruhr-Universität Bochum), Tommaso Frassetto (Technische Universität Darmstadt), Thorsten Holz (Ruhr-Universität Bochum), Patrick Jauernig (Technische Universität Darmstadt), Ahmad-Reza Sadeghi (Technische Universität Darmstadt), Daniel Teuchert (Ruhr-Universität Bochum)

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Ruian Duan (Georgia Institute of Technology), Ashish Bijlani (Georgia Institute of Technology), Yang Ji (Georgia Institute of Technology), Omar Alrawi (Georgia Institute of Technology), Yiyuan Xiong (Peking University), Moses Ike (Georgia Institute of Technology), Brendan Saltaformaggio (Georgia Institute of Technology), Wenke Lee (Georgia Institute of Technology)

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Statistical Privacy for Streaming Traffic

Xiaokuan Zhang (The Ohio State University), Jihun Hamm (The Ohio State University), Michael K. Reiter (University of North Carolina at Chapel Hill), Yinqian Zhang (The Ohio State University)

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