39 citations · 308 across the 129 of their papers we have counts for
15 papers · 1 filter
Improved Differentially Private Algorithms for Rank Aggregation
Quentin Hillebrand, Pasin Manurangsi, Vorapong Suppakitpaisarn +1
Rank aggregation is a task of combining the rankings of items from multiple users into a single ranking that best represents the users' rankings. Alabi et al. (AAAI'22) presents di…
Nearly-Optimal Private Selection via Gaussian Mechanism
Ethan Leeman, Pasin Manurangsi
Steinke (2025) recently asked the following intriguing open question: Can we solve the differentially private selection problem with nearly-optimal error by only (adaptively) invok…
Fair Allocation of Indivisible Goods with Variable Groups
Paul Gölz, Ayumi Igarashi, Pasin Manurangsi +1
We study the fair allocation of indivisible goods with variable groups. In this model, the goal is to partition the agents into groups of given sizes and allocate the goods to the…
Exact zCDP Characterizations for Fundamental Differentially Private Mechanisms
Charlie Harrison, Pasin Manurangsi
Zero-concentrated differential privacy (zCDP) is a variant of differential privacy (DP) that is widely used partly thanks to its nice composition property. While a tight conversion…
VaultGemma: A Differentially Private Gemma Model
Amer Sinha, Thomas Mesnard, Ryan McKenna +18
We introduce VaultGemma 1B, a 1 billion parameter model within the Gemma family, fully trained with differential privacy. Pretrained on the identical data mixture used for the Gemm…
Discrepancy Beyond Additive Functions with Applications to Fair Division
Alexandros Hollender, Pasin Manurangsi, Raghu Meka +1
We consider a setting where we have a ground set together with real-valued set functions , and the goal is to partition into two sets such that $…
Private Hyperparameter Tuning with Ex-Post Guarantee
Badih Ghazi, Pritish Kamath, Alexander Knop +3
The conventional approach in differential privacy (DP) literature formulates the privacy-utility trade-off with a "privacy-first" perspective: for a predetermined level of privacy,…
Dividing Conflicting Items Fairly
Ayumi Igarashi, Pasin Manurangsi, Hirotaka Yoneda
We study the allocation of indivisible goods under conflicting constraints, represented by a graph. In this framework, vertices correspond to goods and edges correspond to conflict…
Urania: Differentially Private Insights into AI Use
Daogao Liu, Edith Cohen, Badih Ghazi +8
We introduce , a novel framework for generating insights about LLM chatbot interactions with rigorous differential privacy (DP) guarantees. The framework employs a private…
Asymptotic Analysis of Weighted Fair Division
Pasin Manurangsi, Warut Suksompong, Tomohiko Yokoyama
Several resource allocation settings involve agents with unequal entitlements represented by weights. We analyze weighted fair division from an asymptotic perspective: if items…
Asymptotic Fair Division: Chores Are Easier Than Goods
Pasin Manurangsi, Warut Suksompong
When dividing items among agents, two of the most widely studied fairness notions are envy-freeness and proportionality. We consider a setting where chores are allocated to …
Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High Regime
Charlie Harrison, Pasin Manurangsi
Differential privacy (DP) can be achieved in a distributed manner, where multiple parties add independent noise such that their sum protects the overall dataset with DP. A common t…
Tight Lower Bound for Multicolor Discrepancy
Pasin Manurangsi, Raghu Meka
We prove the following asymptotically tight lower bound for -color discrepancy: For any , there exists a hypergraph with hyperedges such that its -color discrep…
PREM: Privately Answering Statistical Queries with Relative Error
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +4
We introduce (Private Relative Error Multiplicative weight update), a new framework for generating synthetic data that achieves a relative error guarantee for stati…
Linear-Time User-Level DP-SCO via Robust Statistics
Badih Ghazi, Ravi Kumar, Daogao Liu +1
User-level differentially private stochastic convex optimization (DP-SCO) has garnered significant attention due to the paramount importance of safeguarding user privacy in modern…