21 citations · 46 across the 39 of their papers we have counts for
9 papers · 1 filter
Differential Privacy Overview and Fundamental Techniques
Ferdinando Fioretto, Pascal Van Hentenryck, Juba Ziani
This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice" and provides an introduction to Differential Privacy. It sta…
Algorithmic Collusion Without Threats
Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan +2
There has been substantial recent concern that pricing algorithms might learn to ``collude.'' Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, i…
Differentially Private Data Release on Graphs: Inefficiencies and Unfairness
Ferdinando Fioretto, Diptangshu Sen, Juba Ziani
Networks are crucial components of many sectors, including telecommunications, healthcare, finance, energy, and transportation.The information carried in such networks often contai…
On Rider Strategic Behavior in Ride-Sharing Platforms
Jay Mulay, Diptangshu Sen, Juba Ziani
Over the past decade, ride-sharing services have become increasingly important, with U.S. market leaders such as Uber and Lyft expanding to over 900 cities worldwide and facilitati…
Fairness Issues and Mitigations in (Differentially Private) Socio-Demographic Data Processes
Joonhyuk Ko, Juba Ziani, Saswat Das +2
Statistical agencies rely on sampling techniques to collect socio-demographic data crucial for policy-making and resource allocation. This paper shows that surveys of important soc…
Equilibria of Data Marketplaces with Privacy-Aware Sellers under Endogenous Privacy Costs
Diptangshu Sen, Jingyan Wang, Juba Ziani
We study a two-sided online data ecosystem comprised of an online platform, users on the platform, and downstream learners or data buyers. The learners can buy user data on the pla…
Bayesian Strategic Classification
Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl +2
In strategic classification, agents modify their features, at a cost, to ideally obtain a positive classification from the learner's classifier. The typical response of the learner…
Personalized Differential Privacy for Ridge Regression
Krishna Acharya, Franziska Boenisch, Rakshit Naidu +1
The increased application of machine learning (ML) in sensitive domains requires protecting the training data through privacy frameworks, such as differential privacy (DP). DP requ…
Producers Equilibria and Dynamics in Engagement-Driven Recommender Systems
Krishna Acharya, Varun Vangala, Jingyan Wang +1
Online platforms such as YouTube, Instagram heavily rely on recommender systems to decide what content to present to users. Producers, in turn, often create content that is likely…