PPMLP 2020:隐私保护机器学习实践研讨会

Benyu Zhang, M. Zaharia, S. Ji, R. A. Popa, G. Gu
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摘要

随着科技的飞速发展,数据变得无处不在。近年来,用户隐私和数据安全备受关注,特别是随着欧盟《通用数据保护条例》(GDPR)和其他法律的生效。一方面,从客户的角度出发,如何在利用客户的同时保护用户隐私?数据是一项具有挑战性的任务。另一方面,数据孤岛正在成为社会上最突出的问题之一。生意上的?如何在满足数据隐私和监管合规要求的同时,弥合这些孤立的数据孤岛,构建更好的人工智能系统,这对传统的机器学习范式提出了巨大的挑战。PPMLP将提供一个机会,将CCS社区和机器学习社区的研究人员联系起来,共同应对这些挑战。
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PPMLP 2020: Workshop on Privacy-Preserving Machine Learning In Practice
With the rapid development of technology, data is becoming ubiquitous. User privacy and data security are drawing much attention over the recent years, especially with the European Union's General Data Protection Regulation (GDPR) and other laws coming into force. On one hand, from the customers' perspective, how to protect user privacy while making use of customers? data is a challenging task. On the other hand, data silos are becoming one of the most prominent issues for the society. From the business? perspective, how to bridge these isolated data islands to build better AI systems while meeting the data privacy and regulatory compliance requirements has imposed great challenges to the traditional machine learning paradigm. PPMLP will provide an opportunity to connect researchers from both CCS community and machine learning community to tackle these challenges.
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Session details: Session 1D: Applied Cryptography and Cryptanalysis HACLxN: Verified Generic SIMD Crypto (for all your favourite platforms) Pointproofs: Aggregating Proofs for Multiple Vector Commitments Session details: Session 4D: Distributed Protocols A Performant, Misuse-Resistant API for Primality Testing
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