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* [Caml-list] Call for Participation: The AAAI-20 Workshop on Privacy-Preserving Artificial Intelligence
@ 2020-01-04 17:34 Fioretto, Ferdinando
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Apologies for cross-posting - Please forward to anybody who might be interested

The AAAI-20 Workshop on Privacy-Preserving Artificial Intelligence [Call for Participation]

Workshop URL: https://www2.isye.gatech.edu/~fferdinando3/cfp/PPAI20
Registration URL: https://aaai.org/Conferences/AAAI-20/registration/
Online Registration Deadline: January 10, 2020
Location: AAAI 2020 - Hilton New York Midtown, New York, NY, USA
Date: February 7, 2020 (Full day)

Scope

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.

Technical Program
• 8:45 - 9:00: Poster Setup and Opening Statement
• 9:00 - 9:45: Invited Talk: Catuscia Palamidessi
• 9:45 - 10:30: Session I
Session Chair: TBA
• Gilie Gefen, Omer Ben-Porat, Moshe Tennenholtz and Elad Yom-Tov.
Privacy, altruism, and experience: Estimating the perceived value of Internet data for medical uses.
• Reza Shokri, Martin Strobel and Yair Zick.
Exploiting Transparency Measures for Membership Inference: a Cautionary Tale.
• Shubhankar Mohapatra, Xi He, Gautam Kamath and Om Thakkar.
Diffindo! Differentially Private Learning with Noisy Labels.
• 10:30 - 11:00: Break and Poster Session
• 11:00 - 11:45: Invited Talk: Boi Faltings
• 11:45 - 12:30: Poster Session
• 12:30 - 14:00: Lunch (not sponsored)
• 14:00 - 14:45: Invited Talk: Aleksandar Nikolov
• 14:45 - 15:30: Session II
Session Chair: TBA
• Kai Wen Wang, Travis Dick and Maria-Florina Balcan.
Scalable and provably accurate algorithms for differentially private distributed decision tree learning.
• Chaitali Ashok Choudhary, Martine De Cock, Rafael Dowsley, Anderson Nascimento and Davis Railsback.
Secure Training of Extra Trees Classifiers over Continuous Data.
• Dominik Fay, Jens Sjölund and Tobias J. Oechtering.
Private Learning for High-Dimensional Targets with PATE.
• 15:30 - 16:00: Break and Poster Session
• 16:00 - 17:00: Poster Session
• 17:00 - 18:00: Panel Discussion

Accepted Poster Presentations
• Qiu Yuchen, Yuanyuan Qiao, Aimin Zhang and Jie Yang
Residence and Workplace Recovery: User Privacy Risk in Mobility Data
• Hanten Chang and Hiroyasu Ando
Privacy Preserving Data Sharing by Integrating Perturbed Distance Matrices
• Shreya Sharma, Xing Chaoping and Yang Liu
Privacy-Preserving Deep Learning with SPDZ
• Liyue Fan
A Survey of Differentially Private Generative Adversarial Networks
• Colin Wan, Zheng Li, Alicia Guo and Yue Zhao
SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula
• Ashish Dandekar, Debabrota Basu and Stephane Bressan
Differential Privacy at Risk: Bridging Randomness and Privacy Budget
• Ulrich Aïvodji, Sébastien Gambs and Timon Ther
GAMIN: An Adversarial Approach to Black-Box Model Inversion
• Longfei Zheng, Chaochao Chen, Yingting Liu, Bingzhe Wu, Xibin Wu, Li Wang, Lei Wang and Jun Zhou
Industrial Scale Privacy Preserving Deep Neural Network
• Yingting Liu, Chaochao Chen, Longfei Zheng, Li Wang and Jun Zhou
Privacy Preserving PCA for Multiparty Modeling
• Clémence Mauger, Gaël Le Mahec and Gilles Dequen
Modeling and Evaluation of k-anonymization Metrics
• Aleksei Triastcyn and Boi Faltings
Bayesian Differential Privacy for Machine Learning
• Himanshu Arora
Guided PATE for Scalable Learning
• Adam Richardson, Aris Filos-Ratsikas, Ljubomir Rokvic and Boi Faltings
Privately Computing Influence in Regression Models
• Hui Hu and Chao Lan
Inference Attack and Defense Mechanisms on the Distributed Private Fair Machine Learning Framework
• Yulin Zhang and Dylan Shell
Plans that Remain Private Even in Hindsight
• Junhong Cheng, Wenyan Liu, Xiaoling Wang, Xingjian Lu, Jing Feng and Yi Li
Adaptive Distributed Differential Privacy with SGD

Invited Speakers
·       Boi Faltings (EPFL)
·       Aleksandar Nikolov (University of Toronto)
·       Catuscia Palamidessi (INRIA)

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 Chairs
·       Ferdinando Fioretto (Syracuse University)
·       Pascal Van Hentenryck (Georgia Institute of Technology)
·       Rachel Cummings (Georgia Institute of Technology)

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<font size="2" class="">Apologies for cross-posting - Please forward to anybody who might&nbsp;be interested<br class="">
<br class="">
</font>
<div class=""><font size="2" class=""><b class="">The AAAI-20&nbsp;Workshop on Privacy-Preserving Artificial Intelligence [Call for Participation]</b><br class="">
</font><span style="font-size: small;" class=""><br class="">
</span></div>
<div class=""><span style="font-size: small;" class="">Workshop URL:&nbsp;</span><span style="font-size: small;" class=""><a href="https://www2.isye.gatech.edu/~fferdinando3/cfp/PPAI20" class="">https://www2.isye.gatech.edu/~fferdinando3/cfp/PPAI20</a></span></div>
<div class=""><font size="2" class="">Registration URL:&nbsp;<a href="https://aaai.org/Conferences/AAAI-20/registration/" class="">https://aaai.org/Conferences/AAAI-20/registration/</a>&nbsp;</font></div>
<div class=""><font size="2" class="">Online Registration Deadline: January 10, 2020</font></div>
<div class=""><span style="font-size: small;" class="">Location</span><span style="font-size: small;" class="">: AAAI 2020 - Hilton New York Midtown, New York, NY, USA</span><span style="font-size: small;" class="">&nbsp;</span></div>
<div class=""><span style="font-size: small;" class="">Date: February 7, 2020 </span>
<span style="font-size: small;" class="">(Full day)</span><font size="2" class=""><br class="">
</font><span style="font-size: small;" class=""><br class="">
</span></div>
<div class=""><span style="font-size: small;" class=""><b class="">Scope</b></span></div>
<div class=""><span style="font-size: small;" class="">&nbsp;</span></div>
<div class=""><font size="2" class="">The availability of&nbsp;massive amounts of data, coupled with high-performance cloud computing&nbsp;platforms, has driven significant progress in&nbsp;artificial intelligence and, in&nbsp;particular, machine learning and optimization. Indeed,
 much scientific and&nbsp;technological growth in recent&nbsp;years, including in computer vision, natural&nbsp;language processing, transportation, and health, has been driven by large-scale&nbsp;data sets&nbsp;which provide a strong basis to improve existing algorithms and&nbsp;develop
 new ones. However, due to their large-scale and longitudinal&nbsp;collection, archiving these data sets raise significant privacy concerns. They&nbsp;often reveal sensitive personal information that can be&nbsp;exploited, without the&nbsp;knowledge and/or consent of the involved
 individuals, for various purposes&nbsp;including monitoring, discrimination,&nbsp;and illegal activities.<br class="">
&nbsp;<br class="">
The goal of the AAAI-20&nbsp;Workshop on&nbsp;Privacy-Preserving Artificial Intelligence&nbsp;is to provide a platform for&nbsp;researchers to discuss&nbsp;problems and present solutions related to privacy issues&nbsp;arising within AI applications. The workshop will focus on both theoretical
 and&nbsp;practical challenges arising in the design of privacy-preserving AI systems and&nbsp;algorithms. It will place particular emphasis on algorithmic&nbsp;approaches to&nbsp;protect data privacy in the context of learning, optimization, and decision&nbsp;making that raise fundamental
 challenges for&nbsp;existing technologies.&nbsp;Additionally, it will welcome algorithms and frameworks to release&nbsp;privacy-preserving benchmarks and datasets.<br class="">
<br class="">
<b class="">Technical Program</b></font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;8:45 - 9:00:&nbsp;Poster Setup and Opening Statement<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;9:00 - 9:45:&nbsp;Invited Talk: Catuscia Palamidessi<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;9:45 - 10:30:&nbsp;Session I<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>Session Chair: TBA<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;Gilie Gefen, Omer Ben-Porat, Moshe Tennenholtz and Elad Yom-Tov.</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>Privacy, altruism, and experience: Estimating the perceived value of Internet data&nbsp;for medical uses.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;Reza Shokri, Martin Strobel and Yair Zick.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>Exploiting Transparency Measures for Membership Inference: a Cautionary Tale.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;Shubhankar Mohapatra, Xi He, Gautam Kamath and Om Thakkar.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>Diffindo! Differentially Private Learning with Noisy Labels.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;10:30 - 11:00:&nbsp;Break and Poster Session<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;11:00 - 11:45:&nbsp;Invited Talk: Boi Faltings<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;11:45 - 12:30:&nbsp;Poster Session<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;12:30 - 14:00:&nbsp;Lunch&nbsp;(not sponsored)<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;14:00 - 14:45:&nbsp;Invited Talk: Aleksandar Nikolov<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;14:45 - 15:30:&nbsp;Session II<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>Session Chair: TBA<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;Kai Wen Wang, Travis Dick and Maria-Florina Balcan.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>Scalable and provably accurate algorithms for differentially private distributed&nbsp;decision tree learning.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;Chaitali Ashok Choudhary, Martine De Cock, Rafael Dowsley, Anderson&nbsp;Nascimento and Davis Railsback.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>Secure Training of Extra Trees Classifiers over Continuous Data.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;Dominik Fay, Jens Sjölund and Tobias J. Oechtering.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>Private Learning for High-Dimensional Targets with PATE.<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;15:30 - 16:00:&nbsp;Break and Poster Session<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;16:00 - 17:00:&nbsp;Poster Session<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>•&nbsp;17:00 - 18:00: Panel Discussion<span class="Apple-tab-span" style="white-space:pre">
</span><br class="">
<br class="">
</font></div>
<div class=""><font size="2" class=""><b class="">Accepted Poster Presentations</b></font></div>
<span class="">
<div class=""><span class="Apple-tab-span" style="white-space:pre"></span>• <font size="2" class="">
Qiu Yuchen, Yuanyuan Qiao, Aimin Zhang and Jie Yang<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Residence and Workplace Recovery: User Privacy Risk in Mobility Data</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Hanten Chang and Hiroyasu Ando<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Privacy Preserving Data Sharing by Integrating Perturbed Distance Matrices</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Shreya Sharma, Xing Chaoping and Yang Liu<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Privacy-Preserving Deep Learning with SPDZ</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Liyue Fan<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">A Survey of Differentially Private Generative Adversarial Networks</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Colin Wan, Zheng Li, Alicia Guo and Yue Zhao<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Ashish Dandekar, Debabrota Basu and Stephane Bressan<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span>Differential Privacy at Risk: Bridging Randomness and Privacy Budget<br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Ulrich Aïvodji, Sébastien Gambs and Timon Ther<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">GAMIN: An Adversarial Approach to Black-Box Model Inversion</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Longfei Zheng, Chaochao Chen, Yingting Liu, Bingzhe Wu, Xibin Wu, Li Wang, Lei&nbsp;Wang and Jun Zhou<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Industrial Scale Privacy Preserving Deep Neural Network</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Yingting Liu, Chaochao Chen, Longfei Zheng, Li Wang and Jun Zhou<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Privacy Preserving PCA for Multiparty Modeling</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Clémence Mauger, Gaël Le Mahec and Gilles Dequen<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Modeling and Evaluation of k-anonymization Metrics</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Aleksei Triastcyn and Boi Faltings<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Bayesian Differential Privacy for Machine Learning</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Himanshu Arora<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Guided PATE for Scalable Learning</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Adam Richardson, Aris Filos-Ratsikas, Ljubomir Rokvic and Boi Faltings<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Privately Computing Influence in Regression Models</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Hui Hu and Chao Lan<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Inference Attack and Defense Mechanisms on the Distributed Private Fair Machine&nbsp;Learning Framework</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Yulin Zhang and Dylan Shell<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Plans that Remain Private Even in Hindsight</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space:pre"></span>• Junhong Cheng, Wenyan Liu, Xiaoling Wang, Xingjian Lu, Jing Feng and Yi Li<br class="">
<span class="Apple-tab-span" style="white-space:pre"></span><i class="">Adaptive Distributed Differential Privacy with SGD</i><br class="">
</font></div>
</span>
<div class=""><font size="2" class=""><br class="">
<b class="">Invited&nbsp;Speakers</b></font></div>
<div class=""><font size="2" class="">·&nbsp; &nbsp; &nbsp; &nbsp;Boi Faltings (EPFL)<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Aleksandar Nikolov (University of&nbsp;Toronto)<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Catuscia Palamidessi (INRIA)<br class="">
&nbsp;<br class="">
<b class="">Workshop&nbsp;Committee</b></font></div>
<div class=""><font size="2" class="">·&nbsp; &nbsp; &nbsp; &nbsp;Aws Albarghouthi - University of&nbsp;Wisconsin-Madison<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Carsten Baum - Bar Ilan University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Aurélien Bellet - INRIA<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Elette Boyle - Technion<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Mark Bun - Boston University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Kamalika Chaudhuri - University of&nbsp;California San Diego<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Graham Cormode - The University of&nbsp;Warwick<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Marco Gaboardi - Boston University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Antti Honkela - University of&nbsp;Helsinki<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Peter Kairouz - Google AI<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Kim Laine - Microsoft<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Audra McMillan - Northeastern&nbsp;University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Sebastian Meiser - University&nbsp;College London<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Ilya Mironov - Google<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Aleksandar Nikolov - University of&nbsp;Toronto<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Kobbi Nissim - Georgetown&nbsp;University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Catuscia Palamidessi - INRIA<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Reza Shokri - National University&nbsp;of Singapore<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Jonathan Ullman - Northeastern&nbsp;University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Xiao Wang - Northwestern&nbsp;University<br class="">
<br class="">
</font><b class="">Workshop&nbsp;Chairs</b><br class="">
<span style="font-size: small;" class="">·</span><span style="font-size: small;" class="">&nbsp; &nbsp; &nbsp; &nbsp;</span><span style="font-size: small;" class="">Ferdinando Fioretto (Syracuse University</span><span style="font-size: small;" class="">)</span><br class="">
<span style="font-size: small;" class="">·</span><span style="font-size: small;" class="">&nbsp; &nbsp; &nbsp; &nbsp;</span><span style="font-size: small;" class="">Pascal Van Hentenryck (Georgia</span><span style="font-size: small;" class="">&nbsp;</span><span style="font-size: small;" class="">Institute
 of Technology)</span><br class="">
<span style="font-size: small;" class="">·</span><span style="font-size: small;" class="">&nbsp; &nbsp; &nbsp; &nbsp;</span><span style="font-size: small;" class="">Rachel Cummings (Georgia Institute</span><span style="font-size: small;" class="">&nbsp;</span><span style="font-size: small;" class="">of
 Technology)</span><br class="">
</div>
<div class=""></div>
</body>
</html>

^ permalink raw reply	[flat|nested] 2+ messages in thread

* [Caml-list] Call for Participation: The AAAI-20 Workshop on Privacy-Preserving Artificial Intelligence
@ 2020-01-07 16:03 Fioretto, Ferdinando
  0 siblings, 0 replies; 2+ messages in thread
From: Fioretto, Ferdinando @ 2020-01-07 16:03 UTC (permalink / raw)


[-- Attachment #1: Type: text/plain, Size: 6544 bytes --]

Apologies for cross-posting - Please forward to anybody who might be interested

The AAAI-20 Workshop on Privacy-Preserving Artificial Intelligence [Call for Participation]

Workshop URL: https://www2.isye.gatech.edu/~fferdinando3/cfp/PPAI20
Registration URL: https://aaai.org/Conferences/AAAI-20/registration/
Online Registration Deadline: January 10, 2020
Location: AAAI 2020 - Hilton New York Midtown, New York, NY, USA
Date: February 7, 2020 (Full day)

Scope

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.

Technical Program
• 8:45 - 9:00: Poster Setup and Opening Statement
• 9:00 - 9:45: Invited Talk: Catuscia Palamidessi
• 9:45 - 10:30: Session I
Session Chair: TBA
• Gilie Gefen, Omer Ben-Porat, Moshe Tennenholtz and Elad Yom-Tov.
Privacy, altruism, and experience: Estimating the perceived value of Internet data for medical uses.
• Reza Shokri, Martin Strobel and Yair Zick.
Exploiting Transparency Measures for Membership Inference: a Cautionary Tale.
• Shubhankar Mohapatra, Xi He, Gautam Kamath and Om Thakkar.
Diffindo! Differentially Private Learning with Noisy Labels.
• 10:30 - 11:00: Break and Poster Session
• 11:00 - 11:45: Invited Talk: Boi Faltings
• 11:45 - 12:30: Poster Session
• 12:30 - 14:00: Lunch (not sponsored)
• 14:00 - 14:45: Invited Talk: Aleksandar Nikolov
• 14:45 - 15:30: Session II
Session Chair: TBA
• Kai Wen Wang, Travis Dick and Maria-Florina Balcan.
Scalable and provably accurate algorithms for differentially private distributed decision tree learning.
• Chaitali Ashok Choudhary, Martine De Cock, Rafael Dowsley, Anderson Nascimento and Davis Railsback.
Secure Training of Extra Trees Classifiers over Continuous Data.
• Dominik Fay, Jens Sjölund and Tobias J. Oechtering.
Private Learning for High-Dimensional Targets with PATE.
• 15:30 - 16:00: Break and Poster Session
• 16:00 - 17:00: Poster Session
• 17:00 - 18:00: Panel Discussion

Accepted Poster Presentations
• Qiu Yuchen, Yuanyuan Qiao, Aimin Zhang and Jie Yang
Residence and Workplace Recovery: User Privacy Risk in Mobility Data
• Hanten Chang and Hiroyasu Ando
Privacy Preserving Data Sharing by Integrating Perturbed Distance Matrices
• Shreya Sharma, Xing Chaoping and Yang Liu
Privacy-Preserving Deep Learning with SPDZ
• Liyue Fan
A Survey of Differentially Private Generative Adversarial Networks
• Colin Wan, Zheng Li, Alicia Guo and Yue Zhao
SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula
• Ashish Dandekar, Debabrota Basu and Stephane Bressan
Differential Privacy at Risk: Bridging Randomness and Privacy Budget
• Ulrich Aïvodji, Sébastien Gambs and Timon Ther
GAMIN: An Adversarial Approach to Black-Box Model Inversion
• Longfei Zheng, Chaochao Chen, Yingting Liu, Bingzhe Wu, Xibin Wu, Li Wang, Lei Wang and Jun Zhou
Industrial Scale Privacy Preserving Deep Neural Network
• Yingting Liu, Chaochao Chen, Longfei Zheng, Li Wang and Jun Zhou
Privacy Preserving PCA for Multiparty Modeling
• Clémence Mauger, Gaël Le Mahec and Gilles Dequen
Modeling and Evaluation of k-anonymization Metrics
• Aleksei Triastcyn and Boi Faltings
Bayesian Differential Privacy for Machine Learning
• Himanshu Arora
Guided PATE for Scalable Learning
• Adam Richardson, Aris Filos-Ratsikas, Ljubomir Rokvic and Boi Faltings
Privately Computing Influence in Regression Models
• Hui Hu and Chao Lan
Inference Attack and Defense Mechanisms on the Distributed Private Fair Machine Learning Framework
• Yulin Zhang and Dylan Shell
Plans that Remain Private Even in Hindsight
• Junhong Cheng, Wenyan Liu, Xiaoling Wang, Xingjian Lu, Jing Feng and Yi Li
Adaptive Distributed Differential Privacy with SGD

Invited Speakers
·       Boi Faltings (EPFL)
·       Aleksandar Nikolov (University of Toronto)
·       Catuscia Palamidessi (INRIA)

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 Chairs
·       Ferdinando Fioretto (Syracuse University)
·       Pascal Van Hentenryck (Georgia Institute of Technology)
·       Rachel Cummings (Georgia Institute of Technology)

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<font size="2" class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">Apologies for cross-posting - Please forward to anybody who might&nbsp;be interested<br class="">
<br class="">
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<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><font size="2" class=""><b class="">The AAAI-20&nbsp;Workshop on Privacy-Preserving Artificial Intelligence [Call for Participation]</b><br class="">
</font><span class="" style="font-size: small;"><br class="">
</span></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span class="" style="font-size: small;">Workshop URL:&nbsp;</span><span class="" style="font-size: small;"><a href="https://www2.isye.gatech.edu/~fferdinando3/cfp/PPAI20" class="">https://www2.isye.gatech.edu/~fferdinando3/cfp/PPAI20</a></span></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><font size="2" class="">Registration URL:&nbsp;<a href="https://aaai.org/Conferences/AAAI-20/registration/" class="">https://aaai.org/Conferences/AAAI-20/registration/</a>&nbsp;</font></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><font size="2" class="">Online Registration Deadline: January 10, 2020</font></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span class="" style="font-size: small;">Location</span><span class="" style="font-size: small;">: AAAI 2020 - Hilton New York Midtown, New York, NY, USA</span><span class="" style="font-size: small;">&nbsp;</span></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span class="" style="font-size: small;">Date: February 7, 2020&nbsp;</span><span class="" style="font-size: small;">(Full day)</span><font size="2" class=""><br class="">
</font><span class="" style="font-size: small;"><br class="">
</span></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span class="" style="font-size: small;"><b class="">Scope</b></span></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><span class="" style="font-size: small;">&nbsp;</span></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><font size="2" class="">The availability of&nbsp;massive amounts of data, coupled with high-performance cloud computing&nbsp;platforms, has driven significant progress in&nbsp;artificial intelligence and,
 in&nbsp;particular, machine learning and optimization. Indeed, much scientific and&nbsp;technological growth in recent&nbsp;years, including in computer vision, natural&nbsp;language processing, transportation, and health, has been driven by large-scale&nbsp;data sets&nbsp;which provide
 a strong basis to improve existing algorithms and&nbsp;develop new ones. However, due to their large-scale and longitudinal&nbsp;collection, archiving these data sets raise significant privacy concerns. They&nbsp;often reveal sensitive personal information that can be&nbsp;exploited,
 without the&nbsp;knowledge and/or consent of the involved individuals, for various purposes&nbsp;including monitoring, discrimination,&nbsp;and illegal activities.<br class="">
&nbsp;<br class="">
The goal of the AAAI-20&nbsp;Workshop on&nbsp;Privacy-Preserving Artificial Intelligence&nbsp;is to provide a platform for&nbsp;researchers to discuss&nbsp;problems and present solutions related to privacy issues&nbsp;arising within AI applications. The workshop will focus on both theoretical
 and&nbsp;practical challenges arising in the design of privacy-preserving AI systems and&nbsp;algorithms. It will place particular emphasis on algorithmic&nbsp;approaches to&nbsp;protect data privacy in the context of learning, optimization, and decision&nbsp;making that raise fundamental
 challenges for&nbsp;existing technologies.&nbsp;Additionally, it will welcome algorithms and frameworks to release&nbsp;privacy-preserving benchmarks and datasets.<br class="">
<br class="">
<b class="">Technical Program</b></font></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;8:45 - 9:00:&nbsp;Poster Setup and Opening Statement<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;9:00 - 9:45:&nbsp;Invited Talk: Catuscia Palamidessi<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;9:45 - 10:30:&nbsp;Session I<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>Session Chair: TBA<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;Gilie Gefen, Omer Ben-Porat, Moshe Tennenholtz and Elad Yom-Tov.</font></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>Privacy, altruism, and experience: Estimating the perceived value of Internet data&nbsp;for medical uses.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;Reza Shokri, Martin Strobel and Yair Zick.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>Exploiting Transparency Measures for Membership Inference: a Cautionary Tale.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;Shubhankar Mohapatra, Xi He, Gautam Kamath and Om Thakkar.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>Diffindo! Differentially Private Learning with Noisy Labels.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;10:30 - 11:00:&nbsp;Break and Poster Session<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;11:00 - 11:45:&nbsp;Invited Talk: Boi Faltings<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;11:45 - 12:30:&nbsp;Poster Session<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;12:30 - 14:00:&nbsp;Lunch&nbsp;(not sponsored)<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;14:00 - 14:45:&nbsp;Invited Talk: Aleksandar Nikolov<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;14:45 - 15:30:&nbsp;Session II<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>Session Chair: TBA<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;Kai Wen Wang, Travis Dick and Maria-Florina Balcan.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>Scalable and provably accurate algorithms for differentially private distributed&nbsp;decision tree learning.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;Chaitali Ashok Choudhary, Martine De Cock, Rafael Dowsley, Anderson&nbsp;Nascimento and Davis Railsback.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>Secure Training of Extra Trees Classifiers over Continuous Data.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;Dominik Fay, Jens Sjölund and Tobias J. Oechtering.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>Private Learning for High-Dimensional Targets with PATE.<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;15:30 - 16:00:&nbsp;Break and Poster Session<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;16:00 - 17:00:&nbsp;Poster Session<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;17:00 - 18:00: Panel Discussion<span class="Apple-tab-span" style="white-space: pre;">
</span><br class="">
<br class="">
</font></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><font size="2" class=""><b class="">Accepted Poster Presentations</b></font></div>
<span class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">
<div class=""><span class="Apple-tab-span" style="white-space: pre;"></span>•&nbsp;<font size="2" class="">Qiu Yuchen, Yuanyuan Qiao, Aimin Zhang and Jie Yang<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Residence and Workplace Recovery: User Privacy Risk in Mobility Data</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Hanten Chang and Hiroyasu Ando<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Privacy Preserving Data Sharing by Integrating Perturbed Distance Matrices</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Shreya Sharma, Xing Chaoping and Yang Liu<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Privacy-Preserving Deep Learning with SPDZ</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Liyue Fan<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">A Survey of Differentially Private Generative Adversarial Networks</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Colin Wan, Zheng Li, Alicia Guo and Yue Zhao<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Ashish Dandekar, Debabrota Basu and Stephane Bressan<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span>Differential Privacy at Risk: Bridging Randomness and Privacy Budget<br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Ulrich Aïvodji, Sébastien Gambs and Timon Ther<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">GAMIN: An Adversarial Approach to Black-Box Model Inversion</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Longfei Zheng, Chaochao Chen, Yingting Liu, Bingzhe Wu, Xibin Wu, Li Wang, Lei&nbsp;Wang and Jun Zhou<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Industrial Scale Privacy Preserving Deep Neural Network</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Yingting Liu, Chaochao Chen, Longfei Zheng, Li Wang and Jun Zhou<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Privacy Preserving PCA for Multiparty Modeling</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Clémence Mauger, Gaël Le Mahec and Gilles Dequen<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Modeling and Evaluation of k-anonymization Metrics</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Aleksei Triastcyn and Boi Faltings<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Bayesian Differential Privacy for Machine Learning</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Himanshu Arora<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Guided PATE for Scalable Learning</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Adam Richardson, Aris Filos-Ratsikas, Ljubomir Rokvic and Boi Faltings<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Privately Computing Influence in Regression Models</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Hui Hu and Chao Lan<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Inference Attack and Defense Mechanisms on the Distributed Private Fair Machine&nbsp;Learning Framework</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Yulin Zhang and Dylan Shell<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Plans that Remain Private Even in Hindsight</i><br class="">
</font></div>
<div class=""><font size="2" class=""><span class="Apple-tab-span" style="white-space: pre;"></span>• Junhong Cheng, Wenyan Liu, Xiaoling Wang, Xingjian Lu, Jing Feng and Yi Li<br class="">
<span class="Apple-tab-span" style="white-space: pre;"></span><i class="">Adaptive Distributed Differential Privacy with SGD</i><br class="">
</font></div>
</span>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><font size="2" class=""><br class="">
<b class="">Invited&nbsp;Speakers</b></font></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><font size="2" class="">·&nbsp; &nbsp; &nbsp; &nbsp;Boi Faltings (EPFL)<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Aleksandar Nikolov (University of&nbsp;Toronto)<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Catuscia Palamidessi (INRIA)<br class="">
&nbsp;<br class="">
<b class="">Workshop&nbsp;Committee</b></font></div>
<div class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><font size="2" class="">·&nbsp; &nbsp; &nbsp; &nbsp;Aws Albarghouthi - University of&nbsp;Wisconsin-Madison<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Carsten Baum - Bar Ilan University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Aurélien Bellet - INRIA<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Elette Boyle - Technion<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Mark Bun - Boston University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Kamalika Chaudhuri - University of&nbsp;California San Diego<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Graham Cormode - The University of&nbsp;Warwick<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Marco Gaboardi - Boston University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Antti Honkela - University of&nbsp;Helsinki<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Peter Kairouz - Google AI<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Kim Laine - Microsoft<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Audra McMillan - Northeastern&nbsp;University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Sebastian Meiser - University&nbsp;College London<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Ilya Mironov - Google<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Aleksandar Nikolov - University of&nbsp;Toronto<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Kobbi Nissim - Georgetown&nbsp;University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Catuscia Palamidessi - INRIA<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Reza Shokri - National University&nbsp;of Singapore<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Jonathan Ullman - Northeastern&nbsp;University<br class="">
·&nbsp; &nbsp; &nbsp; &nbsp;Xiao Wang - Northwestern&nbsp;University<br class="">
<br class="">
</font><b class="">Workshop&nbsp;Chairs</b><br class="">
<span class="" style="font-size: small;">·</span><span class="" style="font-size: small;">&nbsp; &nbsp; &nbsp; &nbsp;</span><span class="" style="font-size: small;">Ferdinando Fioretto (Syracuse University</span><span class="" style="font-size: small;">)</span><br class="">
<span class="" style="font-size: small;">·</span><span class="" style="font-size: small;">&nbsp; &nbsp; &nbsp; &nbsp;</span><span class="" style="font-size: small;">Pascal Van Hentenryck (Georgia</span><span class="" style="font-size: small;">&nbsp;</span><span class="" style="font-size: small;">Institute
 of Technology)</span><br class="">
<span class="" style="font-size: small;">·</span><span class="" style="font-size: small;">&nbsp; &nbsp; &nbsp; &nbsp;</span><span class="" style="font-size: small;">Rachel Cummings (Georgia Institute</span><span class="" style="font-size: small;">&nbsp;</span><span class="" style="font-size: small;">of
 Technology)</span></div>
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2020-01-04 17:34 [Caml-list] Call for Participation: The AAAI-20 Workshop on Privacy-Preserving Artificial Intelligence Fioretto, Ferdinando
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