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职业迁徙
个人简介
Research
My research interests lie in identifying and mitigating the potential risks stemming from the use of AI in high-stake decision systems to unleash the full potential of AI while safeguarding our fundamental values and keeping us safe and secure. In particular, I
identify failure modes for AI systems by attacking them in terms of privacy (Mnemonist, Trap weights), fairness (Fairwashing) and security/safety (ColorFool, Mystique, EdgeFool, FilterFool and FoolHD);
mitigate these emerging risks by designing secure and trustworthy (privacy-preserving, robust, fair and explainable) AI to be deployed by institutions (Losing Less, QUOTIENT, DPspeech, GAP, DarkneTZ and Private-Feature Extraction and PrivEdge);
build confidential and reliable auditing frameworks that can be used by the public to audit the trustworthiness of AI-driven services provided by institutions (Confidential-DPproof, Confidential-PROFITT, and Zest).
My research has been published at top-tier conferences including NeurIPS, ICLR, CVPR, CCS, USENIX Security and PETs.
My research interests lie in identifying and mitigating the potential risks stemming from the use of AI in high-stake decision systems to unleash the full potential of AI while safeguarding our fundamental values and keeping us safe and secure. In particular, I
identify failure modes for AI systems by attacking them in terms of privacy (Mnemonist, Trap weights), fairness (Fairwashing) and security/safety (ColorFool, Mystique, EdgeFool, FilterFool and FoolHD);
mitigate these emerging risks by designing secure and trustworthy (privacy-preserving, robust, fair and explainable) AI to be deployed by institutions (Losing Less, QUOTIENT, DPspeech, GAP, DarkneTZ and Private-Feature Extraction and PrivEdge);
build confidential and reliable auditing frameworks that can be used by the public to audit the trustworthiness of AI-driven services provided by institutions (Confidential-DPproof, Confidential-PROFITT, and Zest).
My research has been published at top-tier conferences including NeurIPS, ICLR, CVPR, CCS, USENIX Security and PETs.
研究兴趣
论文共 35 篇作者统计合作学者相似作者
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CoRR (2023)
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PROCEEDINGS OF THE 32ND USENIX SECURITY SYMPOSIUM (2023): 3223-3240
Proc. Priv. Enhancing Technol.no. 1 (2023): 98-114
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UAIpp.1879-1888, (2023)
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Proc. Priv. Enhancing Technol.no. 3 (2023): 307-320
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