Location Privacy in Crowdsourcing
Richard Chbeir, Université de Pau et des Pays de l'Adour (UPPA), France
An Overview of AI & Law Models of Case-Based Reasoning, With Applications in Decision Analysis and Explainable AI
Henry Prakken, Utrecht University, Netherlands
Keynote Lecture
Valentina Presutti, University of Bologna, Italy
Location Privacy in Crowdsourcing
Richard Chbeir
Université de Pau et des Pays de l'Adour (UPPA)
France
http://www.univ-pau.fr/~rchbeir
Brief Bio
Richard Chbeir received his PhD in Computer Science from the University of INSA DE LYON-FRANCE in 2001and then his Habilitation degree in 2010 from the University of Bourgogne. He is currently a Full Professor at the Computer Science Department of University Pau & Pays de l’Adour in Anglet France. He is also leading the research groupe OpenCEMS (https://opencems.sigappfr.org/). His current research interests are in the areas of Data Management, Data semantics, Ontologies, and digital ecosystems. Richard Chbeir has published in international journals, books, and conferences, and has served on the program committees of several international conferences. He is currently the Chair of the French Chapter ACM SIGAPP.
Abstract
The rise of mobile technologies and spatial crowdsourcing platforms has led to a proliferation of geolocated data, widely exploited for applications such as traffic management, urban planning, and task assignment. However, this wealth of information brings significant privacy risks for users, particularly due to inference attacks, which aim to extract sensitive information from recurring spatio-temporal patterns. For example, analyzing a user's regular movements can reveal their home address, workplace, or personal habits such as medical visits, religious practices, or union-related activities. Even after anonymization, cross-referencing datasets is often sufficient to reconstruct an accurate user profile. In this talk, we will explore how traditional privacy-preserving techniques (such as pointwise anonymization, noise addition, or dummy location generation) often prove insufficient. Two main angles will be particularly explored: 1) Data representation and how the identification of Points of Interest (POIs) in considered so far, and 2) Anonymization mechanisms and how existing methods generate fictitious locations. Some results of our research groups will also be presented.
An Overview of AI & Law Models of Case-Based Reasoning, With Applications in Decision Analysis and Explainable AI
Henry Prakken
Utrecht University
Netherlands
Brief Bio
Henry Prakken is a professor of Artificial Intelligence and Law in the Responsible AI group of the Department of Information and Computing Sciences at Utrecht University. He has master degrees in law (1985) and philosophy (1988) from the University of Groningen. In 1993 he obtained his PhD degree (cum laude) at the Free University Amsterdam with a thesis titled Logical Tools for Modelling Legal Argument.
Prakken's main research interests concern artificial intelligence & law and computational models of argumentation. Prakken is a past president of the International Association for AI & Law (IAAIL), of the JURIX Foundation for Legal Knowledge-Based Systems and of the steering committee of the COMMA conferences on Computational Models of Argument. He is on the editorial board of several journals, including Artificial Intelligence and Law. Between 2017-2022 he was an associate editor of Artificial Intelligence.
Abstract
In this talk I will give an overview of recent research of myself and my students on formal and computational models of legal case-based reasoning. Legal case-based reasoning is the process of arguing for or against decisions in new cases by drawing analogies to or stressing differences with precedent cases. In the field of AI & law, seminal work on case-based reasoning was by Ashley & Rissland on the HYPO system, followed by Aleven's work on the CATO system. This work primarily focussed on generating case-based debates. More recently, John Horty initiated the formal study of so-called precedential constraint, which addresses a question of a more logical nature, namely, to what extent a decision in a new case is constrained by a body of precedents.
In our recent work we have further developed this work and, among other things, developed gradual consistency measures for collections of case-based decisions. We have also studied the application of models of legal case-based reasoning in explainable AI, by exploiting an analogy between the legal notion of precedent and the machine-learning concept of training data.
Keynote Lecture
Valentina Presutti
University of Bologna
Italy
![]()
Brief Bio
Valentina is an Associate Professor of Computer Science at the University of Bologna. She is also an Associate Researcher at the Institute of Cognitive Science and Technologies of CNR and coordinator of STLab. She received her Ph.D in Computer Science at the University of Bologna (2006). Her research interests include AI, Semantic Web and Linked Data, Knowledge Extraction, Empirical Semantics, Social Robotics, Ontology and Knowledge Engineering. She coordinates the EUH2020 project Polifonia (2021-2024). She was responsible for several national and EU projects (e.g. MARIO, IKS, ArCo). During her post-doc she worked in NeOn and created ontologydesignpatterns.org and the series WOP, reference resources for semantic web researchers. She has published +150 peer reviewed articles. She is part of the editorial board of J. of Web Semantics (Elsevier), Data Intelligence (MIT Press), JASIST (Wiley), Intelligenza Artificiale (IOS Press), and of "Semantic Web Studies" (IOS Press). She is co-director of International Semantic Web Research Summer School (ISWS) and has served in organisational and scientific roles for several events. Google Profile - https://scholar.google.com/citations?user=dvNHkAwAAAAJ&hl=en