[Om-announce] CFP: Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21)
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Sun Oct 25 16:36:40 CET 2020
The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21)
Workshop URL: https://ppai21.github.io/ <https://ppai21.github.io/>
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 second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21) held at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) builds on the success of last year’s AAAI PPAI 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-21 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) Privacy challenges created by the governments and tech industry response to the Covid-19 outbreak; (3) Social issues related to tracking, tracing, and surveillance programs; and (4) Algorithms and frameworks to release privacy-preserving benchmarks and data sets.
The workshop organizers invite paper submissions on the following (and related) topics:
Applications of privacy-preserving AI systems
Attacks on data privacy
Differential privacy: theory and applications
Distributed privacy-preserving algorithms
Human rights and privacy
Privacy issues related to the Covid-19 outbreak
Privacy policies and legal issues
Privacy preserving optimization and machine learning
Privacy preserving test cases and benchmarks
Surveillance and societal issues
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.
November 9, 2020 – Submission Deadline
November 30, 2020 – Acceptance Notification
February 8 and 9, 2020 – Workshop Date
The workshop will be a one-day and a half meeting. The first session (half day) will be dedicated to privacy challenges, particularly those risen by the Covid-19 pandemic tracing and tracking policy programs. The second, day-long, session will be dedicated to the workshop technical content about privacy-preserving AI. The workshop will include a number of (possibly parallel) technical sessions, a virtual poster session where presenters can discuss their work, with the aim of further fostering collaborations, multiple invited speakers covering crucial challenges for the field of privacy-preserving AI applications, including policy and societal impacts, a number of tutorial talks, and will conclude with a panel discussion.
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/PPAI2021 <https://cmt3.research.microsoft.com/PPAI2021>
Submissions of technical papers can be up to 7 pages excluding references and appendices. Short or position papers of up to 4 pages are also welcome. All papers must be submitted in PDF format, using the AAAI-21 author kit (see the workshop website for more details).
John M. Abowd (US Census Bureau)
Nicolas Papernot (University of Toronto)
Reza Shokri (National University of Singapore)
Steven Wu (Carnegie Mellon University)
Ferdinando Fioretto (Syracuse University)
Pascal Van Hentenryck (Georgia Institute of Technology)
Richard W. Evans (Rice University)
Aws Albarghouthi - University of Wisconsin-Madison
Carsten Baum - Aarhus University
Aurélien Bellet - INRIA
Mark Bun - Boston University
Albert Cheu - Northeastern University
Graham Cormode - University of Warwick
Rachel Cummings - Georgia Tech
Xi He - University of Waterloo
Antti Honkela -University of Helsinki
Mohamed Ali Kaafar - Macquarie University and CSIRO-Data61
Kim Laine - Microsoft Research
Olga Ohrimenko - The University of Melbourne
Catuscia Palamidessi - Laboratoire d'informatique de l'École polytechnique
Marco Romanelli - INRIA
Reza Shokri - NUS
Vikrant Singhal - Northeastern University
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