[Apologies for cross-posting - Please forward to anybody who might be interested]
The AAAI-20 Workshop on Privacy-Preserving Artificial Intelligence 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. Indeed, much scientific and technological growth in recent years, including in computer vision, natural language processing, transportation, and health, has been driven by large-scale data sets which provide a strong basis to improve existing algorithms and develop new ones. However, due to their large-scale and longitudinal collection, archiving these data sets raise significant privacy concerns. They often reveal sensitive personal 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 goal of the AAAI-20 Workshop on Privacy-Preserving Artificial Intelligence is to provide a platform for researchers to discuss problems and present solutions related to privacy issues arising within AI applications. The workshop will focus on both theoretical and practical challenges arising in the design of privacy-preserving AI systems and algorithms. It will place particular emphasis on algorithmic approaches to protect data privacy in the context of learning, optimization, and decision making that raise fundamental challenges for existing technologies. Additionally, it will welcome algorithms and frameworks to release privacy-preserving benchmarks and datasets. Topics We invite paper submissions on the following (and related) topics: • Applications of privacy-preserving AI systems • Architectures and privacy-preserving learning protocols • Constrained-based approaches to privacy • Differential privacy: theory and applications • Distributed privacy-preserving algorithms • Human-aware private algorithms • Incentive mechanisms and game theory • Privacy-preserving machine learning • Privacy-preserving algorithms for medical applications • Privacy-preserving algorithms for temporal data • Privacy-preserving test cases and benchmarks • Privacy and policy-making • Secure multi-party computation • Secret sharing techniques • Trade-offs between privacy and utility Position, perspective, and vision papers are also welcome. The workshop will welcome papers that describe the release of privacy-preserving benchmarks and datasets 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. Papers accepted in the main conference are also welcome! Important Dates • November 15, 2019 – Submission Deadline • December 4, 2019 – Acceptance Notification • February 7 or 8, 2020 – Workshop Date (Full day) Format The workshop will be a full-day and will include a mix of invited speakers, peer-reviewed papers (talks and poster sessions) and will conclude with a panel discussion. Attendance Attendance is open to all. At least one author of each accepted submission must be present at the workshop. Submission Submission URL: https://easychair.org/conferences/?conf=ppai20 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-20 author kit. Papers will be peer-reviewed and selected for oral and/or poster presentation at the workshop. Workshop Chairs • Ferdinando Fioretto (Georgia Institute of Technology) • Pascal Van Hentenryck (Georgia Institute of Technology) • Rachel Cummings (Georgia Institute of Technology) Workshop Committee • Aws Albarghouthi - University of Wisconsin-Madison • Carsten Baum - Bar Ilan University • Aurélien Bellet - INRIA • Elette Boyle - Technion • Mark Bun - Boston University • Kamalika Chaudhuri - University of California San Diego • Graham Cormode - The University of Warwick • Marco Gaboardi - Boston University • Antti Honkela - University of Helsinki • Peter Kairouz - Google AI • Kim Laine - Microsoft • Audra McMillan - Northeastern University • Sebastian Meiser - University College London • Ilya Mironov - Google • Aleksandar Nikolov - University of Toronto • Kobbi Nissim - Georgetown University • Catuscia Palamidessi - INRIA • Reza Shokri - National University of Singapore • Jonathan Ullman - Northeastern University • Xiao Wang - Northwestern University Workshop URL: https://www2.isye.gatech.edu/~fferdinando3/cfp/PPAI20
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