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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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09/18/2023
Distributionally Time-Varying Online Stochastic Optimization under Polyak-Łojasiewicz Condition with Application in Conditional Value-at-Risk Statistical Learning
In this work, we consider a sequence of stochastic optimization problems...
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07/24/2023
Maximal Quantum Information Leakage
A new measure of information leakage for quantum encoding of classical d...
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02/24/2023
Privacy Against Hypothesis-Testing Adversaries for Quantum Computing
A novel definition for data privacy in quantum computing based on quantu...
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02/15/2023
Distributionally-Robust Optimization with Noisy Data for Discrete Uncertainties Using Total Variation Distance
Stochastic programs where the uncertainty distribution must be inferred ...
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11/01/2021
Safe Learning of Linear Time-Invariant Systems
We consider safety in simultaneous learning and control of discrete-time...
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10/11/2021
Optimal Stochastic Evasive Maneuvers Using the Schrodinger's Equation
In this paper, preys with stochastic evasion policies are considered. Th...
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08/09/2021
Zero-Error Feedback Capacity of Finite-State Additive Noise Channels for Stabilization of Linear Systems
It is known that for a discrete channel with correlated additive noise, ...
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06/18/2021
Sharing in a Trustless World: Privacy-Preserving Data Analytics with Potentially Cheating Participants
Lack of trust between organisations and privacy concerns about their dat...
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03/02/2021
Safe Learning of Uncertain Environments for Nonlinear Control-Affine Systems
In many learning based control methodologies, learning the unknown dynam...
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01/24/2021
A Linear Reduction Method for Local Differential Privacy and Log-lift
This paper considers the problem of publishing data X while protecting c...
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01/18/2021
Optimal Pre-Processing to Achieve Fairness and Its Relationship with Total Variation Barycenter
We use disparate impact, i.e., the extent that the probability of observ...
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11/30/2020
Gradient Sparsification Can Improve Performance of Differentially-Private Convex Machine Learning
We use gradient sparsification to reduce the adverse effect of different...
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11/24/2020
When Machine Learning Meets Privacy: A Survey and Outlook
The newly emerged machine learning (e.g. deep learning) methods have bec...
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10/20/2020
Non-Stochastic Private Function Evaluation
We consider private function evaluation to provide query responses based...
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08/28/2020
Deconvoluting Kernel Density Estimation and Regression for Locally Differentially Private Data
Local differential privacy has become the gold-standard of privacy liter...
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08/11/2020
Security Versus Privacy
Linear queries can be submitted to a server containing private data. The...
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06/24/2020
Distributionally-Robust Machine Learning Using Locally Differentially-Private Data
We consider machine learning, particularly regression, using locally-dif...
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06/02/2020
Online Stochastic Convex Optimization: Wasserstein Distance Variation
Distributionally-robust optimization is often studied for a fixed set of...
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05/29/2020
An Explicit Formula for the Zero-Error Feedback Capacity of a Class of Finite-State Additive Noise Channels
It is known that for a discrete channel with correlated additive noise, ...
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04/23/2020
Measuring Information Leakage in Non-stochastic Brute-Force Guessing
We propose an operational measure of information leakage in a non-stocha...
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03/24/2020
Bounded state Estimation over Finite-State Channels: Relating Topological Entropy and Zero-Error Capacity
We investigate bounded state estimation of linear systems over finite-st...
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03/18/2020
Predicting Performance of Asynchronous Differentially-Private Learning
We consider training machine learning models using Training data located...
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02/17/2020
Data and Model Dependencies of Membership Inference Attack
Machine Learning (ML) techniques are used by most data-driven organisati...
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01/29/2020
Regularization Helps with Mitigating Poisoning Attacks: Distributionally-Robust Machine Learning Using the Wasserstein Distance
We use distributionally-robust optimization for machine learning to miti...
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01/29/2020
Modelling and Quantifying Membership Information Leakage in Machine Learning
Machine learning models have been shown to be vulnerable to membership i...
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12/29/2019
Privacy-Preserving Public Release of Datasets for Support Vector Machine Classification
We consider the problem of publicly releasing a dataset for support vect...
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11/12/2019
Developing Non-Stochastic Privacy-Preserving Policies Using Agglomerative Clustering
We consider a non-stochastic privacy-preserving problem in which an adve...
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10/29/2019
Noiseless Privacy
In this paper, we define noiseless privacy, as a non-stochastic rival to...
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10/05/2019
A Fundamental Bound on Performance of Non-Intrusive Load Monitoring with Application to Smart Meter Privacy
We prove that the expected estimation error of non-intrusive load monito...
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09/25/2019
Differential Privacy for Evolving Almost-Periodic Datasets with Continual Linear Queries: Application to Energy Data Privacy
For evolving datasets with continual reports, the composition rule for d...
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08/14/2019
Taking a Lesson from Quantum Particles for Statistical Data Privacy
Privacy is under threat from artificial intelligence revolution fueled b...
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08/12/2019
Discounted Differential Privacy: Privacy of Evolving Datasets over an Infinite Horizon
In this paper, we define discounted differential privacy, as an alternat...
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06/24/2019
A Game-Theoretic Approach to Adversarial Linear Support Vector Classification
In this paper, we employ a game-theoretic model to analyze the interacti...
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06/24/2019
The Value of Collaboration in Convex Machine Learning with Differential Privacy
In this paper, we apply machine learning to distributed private data own...
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04/16/2019
Non-Stochastic Hypothesis Testing with Application to Privacy Against Hypothesis-Testing Adversary
In this paper, we consider privacy against hypothesis testing adversarie...
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02/19/2019
Implementing Homomorphic Encryption Based Secure Feedback Control for Physical Systems
This paper is about an encryption based approach to the secure implement...
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02/02/2019
State Estimation over Worst-Case Erasure and Symmetric Channels with Memory
Worst-case models of erasure and symmetric channels are investigated, in...
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12/11/2018
Secure and Private Implementation of Dynamic Controllers Using Semi-Homomorphic Encryption
This paper presents a secure and private implementation of linear time-i...
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10/26/2018
Development and Analysis of Deterministic Privacy-Preserving Policies Using Non-Stochastic Information Theory
A non-stochastic privacy metric using non-stochastic information theory ...
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08/28/2018