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.
- Algorithmic approaches to protect data privacy in the context of learning, optimization, and decision making that raise fundamental challenges for existing technologies.
- Privacy challenges created by the governments and tech industry response to the Covid-19 outbreak.
- Social issues related to tracking, tracing, and surveillance programs.
- Algorithms and frameworks to release privacy-preserving benchmarks and data sets.
TopicsThe 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.
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.
AttendanceAttendance 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
- Technical Papers: Full-length research papers of up to 7 pages (excluding references and appendices) detailing high quality work in progress or work that could potentially be published at a major conference.
- Short Papers: Position or short papers of up to 4 pages (excluding references and appendices) that describe initial work or the release of privacy-preserving benchmarks and datasets on the topics of interest.
- Technical Track: This track is dedicated to the privacy-preserving AI technical content. It welcomes research contributions centered around the topics described above.
- Privacy Challenges and Social Issues Track: This track is dedicated to discussion of privacy challenges, particularly those risen by the Covid-19 pandemic tracing and tracking policy programs. It welcomes both technical contributions and position papers.
All papers must be submitted in PDF format, using the AAAI-21 author kit.
Submissions should include the name(s), affiliations, and email addresses of all authors.
Submissions will be refereed on the basis of technical quality, novelty, significance, and clarity. Each submission will be thoroughly reviewed by at least two program committee members.
Submissions of papers rejected from the AAAI 2021 technical program are welcomed.
For questions about the submission process, contact the workshop chairs.
- November 9, 2020 – Submission Deadline
- November 30, 2020 – Acceptance Notification
- February 8 and 9, 2020 – Workshop Date
- 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
- Sahib Singh - Ford and OpenMined
- Vikrant Singhal - Northeastern University