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09/20/2023
Information Leakage from Data Updates in Machine Learning Models
In this paper we consider the setting where machine learning models are ...
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01/31/2023
Certified Robustness of Learning-based Static Malware Detectors
Certified defenses are a recent development in adversarial machine learn...
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01/31/2023
Tight Data Access Bounds for Private Top-k Selection
We study the top-k selection problem under the differential privacy mode...
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01/15/2023
UN Handbook on Privacy-Preserving Computation Techniques
This paper describes privacy-preserving approaches for the statistical a...
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12/07/2022
DDoD: Dual Denial of Decision Attacks on Human-AI Teams
Artificial Intelligence (AI) systems have been increasingly used to make...
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10/17/2022
Verifiable and Provably Secure Machine Unlearning
Machine unlearning aims to remove points from the training dataset of a ...
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08/16/2022
Single Round-trip Hierarchical ORAM via Succinct Indices
Accesses to data stored remotely create a side channel that is known to ...
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07/18/2022
Protecting Global Properties of Datasets with Distribution Privacy Mechanisms
Alongside the rapid development of data collection and analysis techniqu...
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06/20/2022
Walking to Hide: Privacy Amplification via Random Message Exchanges in Network
The *shuffle model* is a powerful tool to amplify the privacy guarantees...
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05/20/2022
Getting a-Round Guarantees: Floating-Point Attacks on Certified Robustness
Adversarial examples pose a security risk as they can alter a classifier...
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12/22/2021
Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data
Longitudinal data tracking under Local Differential Privacy (LDP) is a c...
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12/10/2021
Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems
Differential privacy is a de facto privacy framework that has seen adopt...
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09/28/2020
Oblivious Sampling Algorithms for Private Data Analysis
We study secure and privacy-preserving data analysis based on queries ex...
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09/08/2020
Attribute Privacy: Framework and Mechanisms
Ensuring the privacy of training data is a growing concern since many ma...
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06/25/2020
Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces
The ever-increasing take-up of machine learning techniques requires ever...
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06/12/2020
Dataset-Level Attribute Leakage in Collaborative Learning
Multi-party machine learning allows several parties to build a joint mod...
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12/17/2019
Analyzing Privacy Loss in Updates of Natural Language Models
To continuously improve quality and reflect changes in data, machine lea...
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11/08/2019
Collaborative Machine Learning Markets with Data-Replication-Robust Payments
We study the problem of collaborative machine learning markets where mul...
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01/19/2019
Custodes: Auditable Hypothesis Testing
We present Custodes: a new approach to solving the complex issue of prev...
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01/08/2019
Contamination Attacks and Mitigation in Multi-Party Machine Learning
Machine learning is data hungry; the more data a model has access to in ...
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07/02/2018
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Differential privacy has emerged as the main definition for private data...
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12/21/2017
