activity
20072026
most citedWhat Can We Learn Privately?

45 citations · 115 across the 24 of their papers we have counts for

collaborators

34 papers

cs.DS2026

Query Complexity of Testing Structured Parenthesis Languages

Tim Jackman, Diptaksho Palit, Sofya Raskhodnikova

We study the query complexity of testing membership in structured string languages, focusing on Dyck languages and natural generalizations. A tester receives query access to a word…

cs.DS2026

Local Node Differential Privacy

Sofya Raskhodnikova, Adam Smith, Connor Wagaman +1

We initiate an investigation of node differential privacy for graphs in the local model of private data analysis. In our model, dubbed LNDP*, each node sees its own edge list and r…

cs.DS2025

Homomorphism Testing with Resilience to Online Manipulations

Esty Kelman, Uri Meir, Debanuj Nayak +1

A central challenge in property testing is verifying algebraic structure with minimal access to data. A landmark result addressing this challenge, the linearity test of Blum, Luby,…

cs.DS2025

Fast Agnostic Learners in the Plane

Talya Eden, Ludmila Glinskih, Sofya Raskhodnikova

We investigate the computational efficiency of agnostic learning for several fundamental geometric concept classes in the plane. While the sample complexity of agnostic learning is…

cs.CC2025

Computational Complexity in Property Testing

Renato Ferreira Pinto, Diptaksho Palit, Sofya Raskhodnikova

We initiate a systematic study of the computational complexity of property testing, focusing on the relationship between query and time complexity. While traditional work in proper…

cs.DS2025

Privately Evaluating Untrusted Black-Box Functions

Ephraim Linder, Sofya Raskhodnikova, Adam Smith +1

We provide tools for sharing sensitive data when the data curator does not know in advance what questions an (untrusted) analyst might ask about the data. The analyst can specify a…