Zhiqiang Wu (Changsha University of Science and Technology), Rui Li (Dongguan University of Technology)

Dynamic searchable encryption (DSE) is a user-cloud protocol for searching over outsourced encrypted data. Many current DSE schemes resort to oblivious RAMs (ORAM) to achieve forward privacy and backward privacy, which is a concept to describe security levels of the protocol. We show that, however, most prior ORAM-based DSE suffers from a new problem: it is inefficient to fetch/insert a large set of data blocks. We call this the large-stash eviction problem. To address the problem, we present OBI, a multi-path Oblivious RAM, which accesses multiple tree paths per query for handling a large set of data blocks. We classify traditional tree-based ORAMs as single-path ORAMs if they access a single path per query. OBI has two new high-throughtput multi-path eviction algorithms that are several orders of magnitude more efficient than the well-known PATH-ORAM eviction algorithm when the stash is large. We prove that the proposed multi-path ORAM outperforms the traditional single-path ORAM in terms of local stash size and insertion efficiency. Security analysis shows that OBI is secure under the strong forward and backward security model. OBI can protect the well-known DSE leakage, such as the search pattern and the size pattern. We also show that OBI can be applied to oblivious file systems and oblivious conjunctive-query DSE schemes. We conduct experiments on the Enron dataset. The experimental results demonstrate that OBI is far more efficient than the state-of-the-art ORAM-based DSE schemes.

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] => 66 ) ) ) [post__not_in] => Array ( [0] => 13221 ) )

OptRand: Optimistically Responsive Reconfigurable Distributed Randomness

Adithya Bhat (Purdue University), Nibesh Shrestha (Rochester Institute of Technology), Aniket Kate (Purdue University), Kartik Nayak (Duke University)

Read More

Hope of Delivery: Extracting User Locations From Mobile Instant...

Theodor Schnitzler (Research Center Trustworthy Data Science and Security, TU Dortmund, and Ruhr-Universität Bochum), Katharina Kohls (Radboud University), Evangelos Bitsikas (Northeastern University and New York University Abu Dhabi), Christina Pöpper (New York University Abu Dhabi)

Read More

RR: A Fault Model for Efficient TEE Replication

Baltasar Dinis (Instituto Superior Técnico (IST-ULisboa) / INESC-ID / MPI-SWS), Peter Druschel (MPI-SWS), Rodrigo Rodrigues (Instituto Superior Técnico (IST-ULisboa) / INESC-ID)

Read More

Partitioning Ethereum without Eclipsing It

Hwanjo Heo (ETRI), Seungwon Woo (ETRI/KAIST), Taeung Yoon (KAIST), Min Suk Kang (KAIST), Seungwon Shin (KAIST)

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)