Yuan Xiao (The Ohio State University), Yinqian Zhang (The Ohio State University), Radu Teodorescu (The Ohio State University)

SPEculative Execution side Channel Hardware (SPEECH) Vulnerabilities have enabled the notorious Meltdown, Spectre, and L1 terminal fault (L1TF) attacks. While a number of studies have reported different variants of SPEECH vulnerabilities, they are still not well understood. This is primarily due to the lack of information about microprocessor implementation details that impact the timing and order of various micro-architectural events. Moreover, to date, there is no systematic approach to quantitatively measure SPEECH vulnerabilities on commodity processors.

This paper introduces SPEECHMINER, a software framework for exploring and measuring SPEECH vulnerabilities in an automated manner. SPEECHMINER empirically establishes the link between a novel two-phase fault handling model and the exploitability and speculation windows of SPEECH vulnerabilities. It enables testing of a comprehensive list of exception-triggering instructions under the same software framework, which leverages covert-channel techniques and differential tests to gain visibility into the micro-architectural state changes. We evaluated SPEECHMINER on 9 different processor types, examined 21 potential vulnerability variants, confirmed various known attacks, and identified several new variants.

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Zhongjie Wang (University of California, Riverside), Shitong Zhu (University of California, Riverside), Yue Cao (University of California, Riverside), Zhiyun Qian (University of California, Riverside), Chengyu Song (University of California, Riverside), Srikanth V. Krishnamurthy (University of California, Riverside), Kevin S. Chan (U.S. Army Research Lab), Tracy D. Braun (U.S. Army Research Lab)

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BLAZE: Blazing Fast Privacy-Preserving Machine Learning

Arpita Patra (Indian Institute of Science, Bangalore), Ajith Suresh (Indian Institute of Science, Bangalore)

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