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The AAAI-22 Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22)
[submission deadline extended to November 23, 2021 (see below)]
The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in artificial intelligence and, in particular, machine learning and optimization. It has profoundly impacted several areas, including computer vision, natural language processing, and transportation. However, the use of rich data sets also raises significant privacy concerns: They often reveal personal sensitive information that can be exploited, without the knowledge and/or consent of the involved individuals, for various purposes including monitoring, discrimination, and illegal activities.
The third AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22) held at the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) builds on the success of previous years AAAI PPAI-20 and AAAI PPAI-21 to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. The workshop will focus on both the theoretical and practical challenges related to the design of privacy-preserving AI systems and algorithms and will have strong multidisciplinary components, including soliciting contributions about policy, legal issues, and societal impact of privacy in AI.
PPAI-22 will place particular emphasis on: (1) Algorithmic approaches to protect data privacy in the context of learning, optimization, and decision making that raise fundamental challenges for existing technologies; (2) Social issues related to tracking, tracing, and surveillance programs; and (3) Algorithms and frameworks to release privacy-preserving benchmarks and data sets.
The workshop organizers invite paper submissions on the following (and related) topics:
Finally, the workshop will welcome papers that describe the release of privacy-preserving benchmarks and data sets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples.
Attendance is open to all. At least one author of each accepted submission must be present at the workshop.
Submission URL: https://cmt3.research.microsoft.com/PPAI2022