The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21)
Fully Virtual Workshop - February 8 and 9, 2021
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.
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.
Submission URL: https://cmt3.research.microsoft.com/PPAI2021
Rejected AAAI papers with *average* scores of at least 4.5 may be asubmitted directly to PPAI along with previous reviews. These submissions may go through a light review process or accepted if the provided reviews are judged to meet the workshop standard.
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.
Time | Title | link to video | ||
---|---|---|---|---|
08:50 | 09:00 | Introductory remarks | ||
09:00 | 09:45 | Invited Talk by John M. Abowd | [join] | |
09:45 | 10:00 | Spotlight 1: On the Privacy-Utility Tradeoff in Peer-Review Data Analysis | [pre-recoding available] | |
10:00 | 10:15 | Spotlight 2: Leveraging Public Data in Practical Private Query Release: A Case Study with ACS Data | [pre-recoding available] | |
10:30 | 11:15 | Invited Talk by Aswin Machanavajjhala | [join] | |
Break | ||||
11:20 | 12:50 | Tutorial 1: Intro to DP by Audra McMillen | [join] | |
Break | ||||
13:30 | 13:45 | Spotlight 3: Efficient CNN Building Blocks for Encrypted Data | [pre-recoding available] | |
13:45 | 14:00 | Spotlight 4: Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach | [pre-recoding available] | |
14:00 | 14:15 | Spotlight 5: A variational approach to privacy and fairness | [pre-recoding available] | |
14:15 | 15:00 | Invited Talk by Steven Wu | [join] | |
15:00 | 17:00 | Poster Session 1 | [link to Discord channel] | |
[join] |
Time | Title | Presenter | link to video | |
---|---|---|---|---|
09:00 | 09:45 | Invited Talk | Reza Shokri | [join] |
09:45 | 10:00 | spotlight 7: Coded Machine Unlearning | [pre-recoding available] | |
10:00 | 10:15 | spotlight 8: DART: Data Addition and Removal Trees | [pre-recoding available] | |
10:30 | 11:15 | Invited 5 | Aswin Machanavajjhala | [join] |
Break | ||||
11:20 | 12:50 | Tutorial 2: Federated Learning | [join] | |
Break | ||||
13:30 | 13:45 | spotlight 9: Reducing ReLU Count for Privacy-Preserving CNNs | [pre-recoding available] | |
13:45 | 14:00 | spotlight 10: Output Perturbation for General Differentially Private Convex Optimization with Improved Population Loss Bounds, Runtimes and Applications to Private Adversarial Training | [pre-recoding available] | |
Break | ||||
14:15 | 15:00 | Panel: “Differential Privacy: Implementation, deployment, and receptivity. Where are we and what are we missing?” | [join] | |
15:00 | 17:00 | Poster Session 2 | [link to Discord channel] | |
[join] |
Brendan McMahan (Google),
Kallista Bonawitz (Google),
Peter Kairouz (Google)
(Title and Details TBA)