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 ) )

Do Privacy Labels Answer Users' Privacy Questions?

Shikun Zhang, Norman Sadeh (Carnegie Mellon University)

Read More

Post-GDPR Threat Hunting on Android Phones: Dissecting OS-level Safeguards...

Mark Huasong Meng (National University of Singapore), Qing Zhang (ByteDance), Guangshuai Xia (ByteDance), Yuwei Zheng (ByteDance), Yanjun Zhang (The University of Queensland), Guangdong Bai (The University of Queensland), Zhi Liu (ByteDance), Sin G. Teo (Agency for Science, Technology and Research), Jin Song Dong (National University of Singapore)

Read More

Assessing the Impact of Interface Vulnerabilities in Compartmentalized Software

Hugo Lefeuvre (The University of Manchester), Vlad-Andrei Bădoiu (University Politehnica of Bucharest), Yi Chen (Rice University), Felipe Huici (Unikraft.io), Nathan Dautenhahn (Rice University), Pierre Olivier (The University of Manchester)

Read More

PISE: Protocol Inference using Symbolic Execution and Automata Learning

Ron Marcovich, Orna Grumberg, Gabi Nakibly (Technion, Israel Institute of Technology)

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)