Cryptology ePrint Archive: Report 2018/247

Hardware-Supported ORAM in Effect: Practical Oblivious Search and Update on Very Large Dataset

Thang Hoang and Muslum Ozgur Ozmen and Yeongjin Jang and Attila A. Yavuz

Abstract: The ability to query and update over encrypted data is an essential feature to enable breach- resilient cyber-infrastructures. Statistical attacks on searchable encryption (SE) have demonstrated the importance of sealing information leaks in access patterns. In response to such attacks, the community has proposed the Oblivious Random Access Machine (ORAM). However, due to the logarithmic communication overhead of ORAM, the composition of ORAM and SE is known to be costly in the conventional client-server model, which poses a critical barrier toward its practical adaptations.

In this paper, we propose a novel hardware-supported privacy-enhancing platform called Practical Oblivious Search and Update Platform (POSUP), which enables oblivious keyword search and update operations on large datasets with high efficiency. We harness Intel SGX to realize efficient oblivious data structures for oblivious search/update purposes. We implemented POSUP and evaluated its per- formance on a Wikipedia dataset containing ≥ 229 keyword-file pairs. Our implementation is highly efficient, taking only 1 ms to access a 3 KB block with Circuit-ORAM. Our experiments have shown that POSUP offers up to 70 less end-to-end delay with 100 reduced network bandwidth consump- tion compared with the traditional ORAM-SE composition without secure hardware. POSUP is also at least 4.5 faster for up to 99.5% of keywords that can be searched compared with state-of-the-art Intel SGX-assisted search platforms.

Category / Keywords: Secure Enclaves, Intel SGX, Oblivious Data Structures, Oblivious Search/Update

Original Publication (in the same form): The 19th Privacy Enhancing Technologies Symposium (PETS 2019)

Date: received 5 Mar 2018, last revised 2 Oct 2018

Contact author: hoangmin at oregonstate edu

Available format(s): PDF | BibTeX Citation

Version: 20181002:094953 (All versions of this report)

Short URL: ia.cr/2018/247


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