The one-day workshop focuses on the technical aspects of privacy research with invited and contributed talks by distinguished researchers in the area. We will conclude the workshop with a panel discussion about ethical and regulatory aspects. The programme of the workshop will emphasize the diversity of points of view on the problem of privacy, exemplified by the approaches pursued by specific sub-communities scattered across the different meetings comprising the Federated Artificial Intelligence Meeting. We will also ensure there is ample time for discussions that encourage networking between researches from these different sub-communities, which should result in mutually beneficial new long-term collaborations.
- Catuscia Palamidessi (INRIA)
- Úlfar Erlingsson (Google)
- Pınar Yolum (Utrecht)
In this talk, we propose a variant of LDP suitable for metric spaces, such as location data or energy consumption data, and we show that it provides a much higher utility for the same level of privacy. Furthermore, we discuss algorithms to extract the best possible statistical information from the data obfuscated with this metric variant of LDP.
This talk will discuss our recent work on collaborative privacy management to resolve disputes among users in online social networks, with a focus on argumentation and negotiation. Our work is based on representing each user in an online social network with an agent that is responsible for managing and enforcing its user's privacy constraints. When an agent wants to share a post, an agreement session starts between the agent and other relevant agents. The agents provide each other arguments to express their privacy stance and try to convince each other that their claim is true. At the end of the session, the system decides whether sharing the post is justified according to the provided arguments of the agents.
Grants are available to help partially cover the travel expenses of students and researchers attending the workshop. Each grant will reimburse registration costs and travel expenses up to a maximum of 700 euros. We might be unable to provide awards to all applicants, in which case awards will be determined by the organizers based on the application material.
Applications are due on June 4, 2018.
An application for a travel award will consist of a single PDF file with a justification of financial needs, a summary of research interests, and a brief discussion of why the applicant will benefit from participating in the workshop. Please send your applications to firstname.lastname@example.org with the subject title "PiMLAI Travel Grant".
Call For Papers & Important DatesDownload Full CFP Submit Your Abstract
Abstract submission: May 14, 2018 (11pm59 CET)
Notification of acceptance: May 29, 2018
Late breaking results submissions: June 15, 2018
Notification of acceptance : June 20, 2018
Workshop: July 15, 2018
We invite submissions of recent work on privacy in machine learning and artificial intelligence, both theory and application-oriented. Similarly to how ICML, IJCAI, AAMAS, and other FAIM workshops are organized, all accepted abstracts will be part of a poster session held during the workshop. Additionally, the PC will select a subset of the abstracts for short oral presentations. At least one author of each accepted abstract is expected to represent it at the workshop.
Submissions in the form of extended abstracts must be at most 2 pages long (not including references) and adhere to the ICML format. We do accept submissions of work recently published or currently under review. Submissions do not need to be anonymized. The workshop will not have formal proceedings, but authors of accepted abstracts can choose to have their work published on the workshop webpage.
Solicited topics include, but are not limited to:
Differential privacy: theory, applications, and implementations
Privacy in internet of things and multi-agent systems
Privacy-preserving machine learning
Trade-offs between privacy and utility
Programming languages for privacy-preserving data analysis
Statistical notions of privacy, including relaxations of differential privacy
Empirical and theoretical comparisons between different notions of privacy
Policy-making aspects of data privacy
Secure multi-party computation techniques for machine learning
Learning on encrypted data, homomorphic encryption
Distributed privacy-preserving algorithms
Normative approaches to privacy in AI
Privacy in autonomous systems
Online social networks privacy
- Borja Balle (Amazon Research Cambridge)
- Antti Honkela (University of Helsinki)
- Kamalika Chaudhuri (UCSD CSE)
- Beyza Ermis (Amazon Research Berlin)
- Jose Such (King's College London)
- Mijung Park (MPI Tuebingen)
Contact us: email@example.com