Ioannis Demertzis (University of Maryland), Javad Ghareh Chamani (Hong Kong University of Science and Technology & Sharif University of Technology), Dimitrios Papadopoulos (Hong Kong University of Science and Technology), Charalampos Papamanthou (University of Maryland)

We study the problem of dynamic searchable encryption (DSE) with forward-and-backward privacy. Many DSE schemes have been proposed recently but the most efficient ones have one limitation: they require maintaining an operation counter for each unique keyword, either stored locally at the client or accessed obliviously (e.g., with an oblivious map) at the server, during every operation. We propose three new schemes that overcome the above limitation and achieve constant permanent client storage with improved performance, both asymptotically and experimentally, compared to prior state-of-the-art works. In particular, our first two schemes adopt a "static-to-dynamic" transformation which eliminates the need for oblivious accesses during searches. Due to this, they are the first practical schemes with minimal client storage and non-interactive search. Our third scheme is the first quasi-optimal forward-and-backward DSE scheme with only a logarithmic overhead for retrieving the query result (independently of previous deletions). While it does require an oblivious access during search in order to keep permanent client storage minimal, its practical performance is up to four orders of magnitude better than the best existing scheme with quasi-optimal search.

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Kyungtae Kim (Purdue University), Dae R. Jeong (KAIST), Chung Hwan Kim (NEC Labs America), Yeongjin Jang (Oregon State University), Insik Shin (KAIST), Byoungyoung Lee (Seoul National University)

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Metamorph: Injecting Inaudible Commands into Over-the-air Voice Controlled Systems

Tao Chen (City University of Hong Kong), Longfei Shangguan (Microsoft), Zhenjiang Li (City University of Hong Kong), Kyle Jamieson (Princeton University)

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Not All Coverage Measurements Are Equal: Fuzzing by Coverage...

Yanhao Wang (Institute of Software, Chinese Academy of Sciences), Xiangkun Jia (Pennsylvania State University), Yuwei Liu (Institute of Software, Chinese Academy of Sciences), Kyle Zeng (Arizona State University), Tiffany Bao (Arizona State University), Dinghao Wu (Pennsylvania State University), Purui Su (Institute of Software, Chinese Academy of Sciences)

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Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning

Harsh Chaudhari (Indian Institute of Science, Bangalore), Rahul Rachuri (Aarhus University, Denmark), Ajith Suresh (Indian Institute of Science, Bangalore)

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