A
Arkady Yerukhimovich
Researcher at George Washington University
Publications - 49
Citations - 899
Arkady Yerukhimovich is an academic researcher from George Washington University. The author has contributed to research in topics: Encryption & Cryptography. The author has an hindex of 15, co-authored 47 publications receiving 724 citations. Previous affiliations of Arkady Yerukhimovich include Massachusetts Institute of Technology & United States Naval Academy.
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SoK: Cryptographically Protected Database Search
Benjamin Fuller,Mayank Varia,Arkady Yerukhimovich,Emily Shen,Ariel Hamlin,Vijay Gadepally,Richard Shay,John Darby Mitchell,Robert K. Cunningham +8 more
TL;DR: In this paper, the authors identify the important primitive operations across database paradigms and evaluate the current state of protected search systems in implementing these base operations, and present a roadmap and tools for transforming a protected search system into a protected database.
Proceedings ArticleDOI
SoK: Cryptographically Protected Database Search
Benjamin Fuller,Mayank Varia,Arkady Yerukhimovich,Emily Shen,Ariel Hamlin,Vijay Gadepally,Richard Shay,John Darby Mitchell,Robert K. Cunningham +8 more
TL;DR: An evaluation of the current state of protected search systems and describes the main approaches and tradeoffs for each base operation, which puts protected search in the context of unprotected search, identifying key gaps in functionality.
Proceedings ArticleDOI
Computing on Masked Data: a High Performance Method for Improving Big Data Veracity
Jeremy Kepner,Vijay Gadepally,Pete Michaleas,Nabil Schear,Mayank Varia,Arkady Yerukhimovich,Robert K. Cunningham +6 more
TL;DR: In this paper, a technique called Computing on Masked Data (CMD) is proposed to improve data veracity by allowing computations to be performed directly on masked data and ensuring that only authorized recipients can unmask the data.
Proceedings ArticleDOI
A survey of cryptographic approaches to securing big-data analytics in the cloud
TL;DR: A cloud computing model is introduced that captures a rich class of big-data use-cases and allows reasoning about relevant threats and security goals and three cryptographic techniques that can be used to achieve these goals are surveyed.
Proceedings ArticleDOI
POPE: Partial Order Preserving Encoding
TL;DR: In this article, the authors proposed a new primitive called partial order preserving encoding (POPE) that achieves ideal OPE security with frequency hiding and also leaves a sizable fraction of the data pairwise incomparable.