activity
20142023
most cited"I need a better description'': An Investigation Into User Expectations For Differential Privacy

52 citations · 166 across the 28 of their papers we have counts for

collaborators
Showing cs.DSShow all

7 papers · 1 filter

cs.DS2018

Differentially Private Online Submodular Optimization

Adrian Rivera Cardoso, Rachel Cummings

In this paper we develop the first algorithms for online submodular minimization that preserve differential privacy under full information feedback and bandit feedback. A sequence…

cs.DS2018

Individual Sensitivity Preprocessing for Data Privacy

Rachel Cummings, David Durfee

The sensitivity metric in differential privacy, which is informally defined as the largest marginal change in output between neighboring databases, is of substantial significance i…

cs.DS2018★ 5 cited

Differential Privacy for Growing Databases

Rachel Cummings, Sara Krehbiel, Kevin A. Lai +1

We study the design of differentially private algorithms for adaptive analysis of dynamically growing databases, where a database accumulates new data entries while the analysis is…

cs.DS2016★ 4 cited

Adaptive Learning with Robust Generalization Guarantees

Rachel Cummings, Katrina Ligett, Kobbi Nissim +2

The traditional notion of generalization---i.e., learning a hypothesis whose empirical error is close to its true error---is surprisingly brittle. As has recently been noted in [DF…

cs.DS2015

Coordination Complexity: Small Information Coordinating Large Populations

Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan +2

We initiate the study of a quantity that we call coordination complexity. In a distributed optimization problem, the information defining a problem instance is distributed among $n…

cs.DS2014★ 13 cited

Privacy and Truthful Equilibrium Selection for Aggregative Games

Rachel Cummings, Michael Kearns, Aaron Roth +1

We study a very general class of games --- multi-dimensional aggregative games --- which in particular generalize both anonymous games and weighted congestion games. For any such g…

cs.DS2014★ 1 cited

Online Learning and Profit Maximization from Revealed Preferences

Kareem Amin, Rachel Cummings, Lili Dworkin +2

We consider the problem of learning from revealed preferences in an online setting. In our framework, each period a consumer buys an optimal bundle of goods from a merchant accordi…