EDBT 2026 Demo / reviewers in the wild / expert
Lesia Mitridati
dblp:251/3298
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0003-0060-5969ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Privacy and data protection · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Energy systems and smart grids · 100% | |
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
differential privacy |
0.4 | 1 | 2020 | Differential Privacy for Stackelberg Games · IJCAI 2020 |
Privacy and data protection › differential privacy
privacy mechanism design |
0.4 | 1 | 2020 | Differential Privacy for Stackelberg Games · IJCAI 2020 |
Energy systems and smart grids
electricity market |
0.1 | 1 | 2020 | Differential Privacy for Stackelberg Games · IJCAI 2020 |
Algorithmic game theory and mechanism design
stackelberg game |
0.1 | 1 | 2020 | Differential Privacy for Stackelberg Games · IJCAI 2020 |
Methods — techniques the papers use, named apart from their topics
stackelberg game · 1.3differential privacy · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Differential Privacy for Stackelberg GamesabstractThis paper introduces a differentially private (DP) mechanism to protect the information exchanged during the coordination of sequential and interdependent markets. This coordination represents a classic Stackelberg game and relies on the exchange of sensitive information between the system agents. The paper is motivated by the observation that the perturbation introduced by traditional DP mechanisms fundamentally changes the underlying optimization problem and even leads to unsatisfiable instances. To remedy such limitation, the paper introduces the Privacy-Preserving Stackelberg Mechanism (PPSM), a framework that enforces the notions of feasibility and fidelity (i.e. near-optimality) of the privacy-preserving information to the original problem objective. PPSM complies with the notion of differential privacy and ensures that the outcomes of the privacy-preserving coordination mechanism are close-to-optimality for each agent. Experimental results on several gas and electricity market benchmarks based on a real case study demonstrate the effectiveness of the proposed approach. A full version of this paper [Fioretto et al., 2020b] contains complete proofs and additional discussion on the motivating application. Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck |
IJCAI | 2 |