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.

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 34 ) ) ) [post__not_in] => Array ( [0] => 4564 ) )

TEE-aided Write Protection Against Privileged Data Tampering

Lianying Zhao (Concordia University), Mohammad Mannan (Concordia University)

Read More

Cleaning Up the Internet of Evil Things: Real-World Evidence...

Orcun Cetin (Delft University of Technology), Carlos Gañán (Delft University of Technology), Lisette Altena (Delft University of Technology), Takahiro Kasama (National Institute of Information and Communications Technology), Daisuke Inoue (National Institute of Information and Communications Technology), Kazuki Tamiya (Yokohama National University), Ying Tie (Yokohama National University), Katsunari Yoshioka (Yokohama National University), Michel van Eeten (Delft…

Read More

ExSpectre: Hiding Malware in Speculative Execution

Jack Wampler (University of Colorado Boulder), Ian Martiny (University of Colorado Boulder), Eric Wustrow (University of Colorado Boulder)

Read More

MBeacon: Privacy-Preserving Beacons for DNA Methylation Data

Inken Hagestedt (CISPA Helmholtz Center for Information Security), Yang Zhang (CISPA Helmholtz Center for Information Security), Mathias Humbert (Swiss Data Science Center, ETH Zurich/EPFL), Pascal Berrang (CISPA Helmholtz Center for Information Security), Haixu Tang (Indiana University Bloomington), XiaoFeng Wang (Indiana University Bloomington), Michael Backes (CISPA Helmholtz Center for Information Security)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)