Lesia Mitridati

dblp:251/3298 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Privacy and data protection
differential privacy
0.412020
Differential Privacy for Stackelberg Games · IJCAI 2020
Privacy and data protection › differential privacy
privacy mechanism design
0.412020
Differential Privacy for Stackelberg Games · IJCAI 2020
Energy systems and smart grids
electricity market
0.112020
Differential Privacy for Stackelberg Games · IJCAI 2020
Algorithmic game theory and mechanism design
stackelberg game
0.112020
Differential Privacy for Stackelberg Games · IJCAI 2020

Methods — techniques the papers use, named apart from their topics

stackelberg game · 1.3differential privacy · 1.3
YearPublicationVenuePosition
2020 Differential Privacy for Stackelberg Games
abstract
This 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
IJCAI2