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<div class="" style="word-wrap:break-word">This is an announcement of Francesca Falzon's Candidacy Exam.<br class="">
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Candidate: Francesca Falzon<br class="">
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Date: Friday, March 25, 2022<br class="">
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Time: 10 am CST<br class="">
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Remote Location: <a href="https://uchicago.zoom.us/j/92479122662?pwd=b3QzM3FVS0VrWnBGamJEVjZ6VHBUQT09" class="">https://uchicago.zoom.us/j/92479122662?pwd=b3QzM3FVS0VrWnBGamJEVjZ6VHBUQT09</a><br class="">
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Title: A Combinatorial Approach to Leakage Abuse Attacks and their Mitigation<br class="">
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Abstract: With the rise of remote cloud services and the consequent rise in data breaches, there is an increased need for the secure outsourcing of data. The problem of enabling query processing over encrypted data without decryption is a challenging one, and
approaches ranging from software to hardware solutions have been proposed. In this talk, we take a closer look at a class of solutions that are efficient and deployable in the near-term future and that employ the use of light weight symmetric key primitives.
In exchange for this added efficiency, these schemes leak certain information about the underlying data and queries. We explore the information theoretic limitations of what a passive server-side adversary can learn from this information leakage, and present
practical constructions that minimize leakage while supporting complex queries.<br class="">
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In the first part of this talk, we present one of the first attacks on schemes that support range queries over two-attribute data. We relax the assumptions from prior work and present an order reconstruction attack that requires only a strict subset of the
possible range queries. In the second part of this work, we shift our focus to schemes that support shortest path queries on graph-structured data. We initiate our study by describing a query recovery attack on a graph encryption scheme by Ghosh et al. (AsiaCCS
2021). We then present a modified version of this scheme with reduced leakage at the expense of interactive queries. We support our theoretical results with experiments on real world datasets.<br class="">
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Advisors: Ben Zhao and Roberto Tamassia<br class="">
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Committee Members: Ben Zhao, Roberto Tamassia, Kenneth G. Paterson, Aloni Cohen
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