36 citations · 192 across the 32 of their papers we have counts for
6 papers · 1 filter
DDoD: Dual Denial of Decision Attacks on Human-AI Teams
Benjamin Tag, Niels van Berkel, Sunny Verma +5
Artificial Intelligence (AI) systems have been increasingly used to make decision-making processes faster, more accurate, and more efficient. However, such systems are also at cons…
Verifiable and Provably Secure Machine Unlearning
Thorsten Eisenhofer, Doreen Riepel, Varun Chandrasekaran +3
Machine unlearning aims to remove points from the training dataset of a machine learning model after training: e.g., when a user requests their data to be deleted. While many unlea…
Single Round-trip Hierarchical ORAM via Succinct Indices
William Holland, Olga Ohrimenko, Anthony Wirth
Access patterns to data stored remotely create a side channel that is known to leak information even if the content of the data is encrypted. To protect against access pattern leak…
Protecting Global Properties of Datasets with Distribution Privacy Mechanisms
Michelle Chen, Olga Ohrimenko
We consider the problem of ensuring confidentiality of dataset properties aggregated over many records of a dataset. Such properties can encode sensitive information, such as trade…
Walking to Hide: Privacy Amplification via Random Message Exchanges in Network
Hao Wu, Olga Ohrimenko, Anthony Wirth
The *shuffle model* is a powerful tool to amplify the privacy guarantees of the *local model* of differential privacy. In contrast to the fully decentralized manner of guaranteeing…
Getting a-Round Guarantees: Floating-Point Attacks on Certified Robustness
Jiankai Jin, Olga Ohrimenko, Benjamin I. P. Rubinstein
Adversarial examples pose a security risk as they can alter decisions of a machine learning classifier through slight input perturbations. Certified robustness has been proposed as…