13 citations · 26 across the 8 of their papers we have counts for
10 papers
Quantum Advantage for Two-Party Differential Privacy
Daniel Alabi, Emil T. Khabiboulline
We introduce information-theoretically private quantum protocols for two-party Hamming distance when both parties must output the same estimate. Classically, for input length ,…
Quantum Secure Non-Interactive Reductions
Maxwell Gold, Sarah Hagen, Daniel Alabi +1
Efficient models for secure computation often rely on offline preprocessed correlations, which subsequently enable private online computation from minimal assumptions. Within these…
DIME: Query-Efficient Framework for Membership Inference on Diffusion Models
Tue Do, Daniel Alabi
Membership inference attacks expose whether individual records were used to train a model, yet existing attacks on diffusion models are largely heuristic and can require substantia…
When Does Quantum Differential Privacy Compose?
Daniel Alabi, Theshani Nuradha
Composition is a cornerstone of classical differential privacy, enabling strong end-to-end guarantees for complex algorithms through composition theorems (e.g., basic and advanced)…
Representation of Inorganic Synthesis Reactions and Prediction: Graphical Framework and Datasets
Samuel Andrello, Daniel Alabi, Simon J. L. Billinge
While machine learning has enabled the rapid prediction of inorganic materials with novel properties, the challenge of determining how to synthesize these materials remains largely…
Efficiently Attacking Memorization Scores
Tue Do, Varun Chandrasekaran, Daniel Alabi
Influence estimation tools -- such as memorization scores -- are widely used to understand model behavior, attribute training data, and inform dataset curation. However, recent app…
Bounded Space Differentially Private Quantiles
Daniel Alabi, Omri Ben-Eliezer, Anamay Chaturvedi
Estimating the quantiles of a large dataset is a fundamental problem in both the streaming algorithms literature and the differential privacy literature. However, all existing priv…
The Cost of a Reductions Approach to Private Fair Optimization
Daniel Alabi
Through the lens of information-theoretic reductions, we examine a reductions approach to fair optimization and learning where a black-box optimizer is used to learn a fair model f…
Learning to Prune: Speeding up Repeated Computations
Daniel Alabi, Adam Tauman Kalai, Katrina Ligett +3
It is common to encounter situations where one must solve a sequence of similar computational problems. Running a standard algorithm with worst-case runtime guarantees on each inst…
Unleashing Linear Optimizers for Group-Fair Learning and Optimization
Daniel Alabi, Nicole Immorlica, Adam Tauman Kalai
Most systems and learning algorithms optimize average performance or average loss -- one reason being computational complexity. However, many objectives of practical interest are m…