arXiv subjects

Vassilis Zikas

Publications and source records attributed to Vassilis Zikas.

10 recordsLinked to original sources

arXiv2026

Let's Ask Gauss: Improved One-Run Privacy Auditing

Privacy auditing provides an important safeguard by estimating the actual information leaked by a model, thus ensuring that theoretical privacy guarantees hold in practice. We study empirical privacy auditing for differentially private (DP) machine learning, focusing on efficient one-run methods for mechanisms such as DP-SGD. Prior one-run approaches threshold training examples or "canaries" into binary membership guesses, which discards useful information. We show that, in the white-box DP-SGD setting, canary-aligned signals naturally form a sequence of random variables whose normalized sum is asymptotically Gaussian. Leveraging this distributional perspective, we develop a DP-auditing framework that leads to tighter privacy lower bounds from a single training run.

cs.LG↗

arXiv2026

Sequential Auditing for f-Differential Privacy

We present new auditors to assess Differential Privacy (DP) of an algorithm based on output samples. Such empirical auditors are common to check for algorithmic correctness and implementation bugs. Most existing auditors are batch-based or targeted toward the traditional notion of $(\varepsilon,\delta)$-DP; typically both. In this work, we shift the focus to the highly expressive privacy concept of $f$-DP, in which the entire privacy behavior is captured by a single tradeoff curve. Our auditors detect violations across the full privacy spectrum with statistical significance guarantees, which are supported by theory and simulations. Most importantly, and in contrast to prior work, our auditors do not require a user-specified sample size as an input. Rather, they adaptively determine a near-optimal number of samples needed to reach a decision, thereby avoiding the excessively large sample sizes common in many auditing studies. This reduction in sampling cost becomes especially beneficial for expensive training procedures such as DP-SGD. Our method supports both whitebox and blackbox settings and can also be executed in one-run frameworks.

cs.CR↗

arXiv2025

Game of Trust: How Trustworthy Does Your Blockchain Think You Are?

We investigate how a blockchain can distill the collective belief of its nodes regarding the trustworthiness of a (sub)set of nodes into a {\em reputation system} that reflects the probability of correctly performing a task. To address this question, we introduce a framework that breaks it down into two sub-problems: 1. (Information Extraction): How can the system distill trust information from a function of the nodes' true beliefs? 2. (Incentive Design): How can we incentivize nodes to truthfully report such information? To tackle the first sub-problem, we adapt, in a non-trivial manner, the well-known PageRank algorithm to our problem. For the second, we define a new class of games, called Trustworthy Reputation games (TRep games), which aim to extract the collective beliefs on trust from the actions of rational participants. We then propose a concrete TRep game whose utility function leverages Personalized PageRank and can be instantiated through a straightforward blockchain rewards mechanism. Building on this, we show how the TRep game enables the design of a reputation system. Such systems can enhance the robustness, scalability, and efficiency of blockchain and DeFi solutions. For instance, we demonstrate how such a system can be used within a Proof-of-Reputation blockchain.

cs.GT↗

arXiv2025

Pseudo-Equilibria, or: How to Stop Worrying About Crypto and Just Analyze the Game

We consider the problem of a game theorist analyzing a game that uses cryptographic protocols. Ideally, a theorist abstracts protocols as ideal, implementation-independent primitives, letting conclusions in the "ideal world" carry over to the "real world." This is crucial, since the game theorist cannot--and should not be expected to--handle full cryptographic complexity. In today's landscape, the rise of distributed ledgers makes a shared language between cryptography and game theory increasingly necessary. The security of cryptographic protocols hinges on two types of assumptions: state-of-the-world (e.g., "factoring is hard") and behavioral (e.g., "honest majority"). We observe that for protocols relying on behavioral assumptions (e.g., ledgers), our goal is unattainable in full generality. For state-of-the-world assumptions, we show that standard solution concepts, e.g., ($\epsilon$-)Nash equilibria, are not robust to transfer from the ideal to the real world. We propose a new solution concept: the pseudo-Nash equilibrium. Informally, a profile $s=(s_1,\dots,s_n)$ is a pseudo-Nash equilibrium if, for any player $i$ and deviation $s'_i$ with higher expected utility, $i$'s utility from $s_i$ is (computationally) indistinguishable from that of $s'_i$. Pseudo-Nash is simpler and more accessible to game theorists than prior notions addressing the mismatch between (asymptotic) cryptography and game theory. We prove that Nash equilibria in games with ideal, unbreakable cryptography correspond to pseudo-Nash equilibria when ideal cryptography is instantiated with real protocols (under state-of-the-world assumptions). Our translation is conceptually simpler and more general: it avoids tuning or restricting utility functions in the ideal game to fit quirks of cryptographic implementations. Thus, pseudo-Nash lets us study game-theoretic and cryptographic aspects separately and seamlessly.

cs.GT↗

arXiv2025

General-Purpose $f$-DP Estimation and Auditing in a Black-Box Setting

In this paper we propose new methods to statistically assess $f$-Differential Privacy ($f$-DP), a recent refinement of differential privacy (DP) that remedies certain weaknesses of standard DP (including tightness under algorithmic composition). A challenge when deploying differentially private mechanisms is that DP is hard to validate, especially in the black-box setting. This has led to numerous empirical methods for auditing standard DP, while $f$-DP remains less explored. We introduce new black-box methods for $f$-DP that, unlike existing approaches for this privacy notion, do not require prior knowledge of the investigated algorithm. Our procedure yields a complete estimate of the $f$-DP trade-off curve, with theoretical guarantees of convergence. Additionally, we propose an efficient auditing method that empirically detects $f$-DP violations with statistical certainty, merging techniques from non-parametric estimation and optimal classification theory. Through experiments on a range of DP mechanisms, we demonstrate the effectiveness of our estimation and auditing procedures.

cs.CR↗

arXiv2024

Near-Optimal Communication Byzantine Reliable Broadcast under a Message Adversary

We address the problem of Reliable Broadcast in asynchronous message-passing systems with $n$ nodes, of which up to $t$ are malicious (faulty), in addition to a message adversary that can drop some of the messages sent by correct (non-faulty) nodes. We present a Message-Adversary-Tolerant Byzantine Reliable Broadcast (MBRB) algorithm that communicates ${\cal O}(|m|+nκ)$ bits per node, where $|m|$ represents the length of the application message and $κ=Ω(\log n)$ is a security parameter. This communication complexity is optimal up to the parameter $κ$. This significantly improves upon the state-of-the-art MBRB solution (Albouy, Frey, Raynal, and Taïani, TCS 2023), which incurs communication of ${\cal O}(n|m|+n^2κ)$ bits per node. Our solution sends at most $4n^2$ messages overall, which is asymptotically optimal. Reduced communication is achieved by employing coding techniques that replace the need for all nodes to (re-)broadcast the entire application message $m$. Instead, nodes forward authenticated fragments of the encoding of $m$ using an erasure-correcting code. Under the cryptographic assumptions of threshold signatures and vector commitments, and assuming $n > 3t+2d$, where the adversary drops at most $d$ messages per broadcast, our algorithm allows at least $\ell = n - t - (1 + ε)d$ (for any arbitrarily low $ε> 0$) correct nodes to reconstruct $m$, despite missing fragments caused by the malicious nodes and the message adversary.

cs.DC↗

arXiv2023

Concurrent Asynchronous Byzantine Agreement in Expected-Constant Rounds, Revisited

It is well known that without randomization, Byzantine agreement (BA) requires a linear number of rounds in the synchronous setting, while it is flat out impossible in the asynchronous setting. The primitive which allows to bypass the above limitation is known as oblivious common coin (OCC). It allows parties to agree with constant probability on a random coin, where agreement is oblivious, i.e., players are not aware whether or not agreement has been achieved. The starting point of our work is the observation that no known protocol exists for information-theoretic multi-valued OCC with optimal resiliency in the asynchronous setting (with eventual message delivery). This apparent hole in the literature is particularly problematic, as multi-valued OCC is implicitly or explicitly used in several constructions. In this paper, we present the first information-theoretic multi-valued OCC protocol in the asynchronous setting with optimal resiliency, i.e., tolerating $t < n/3$ corruptions, thereby filling this important gap. Further, our protocol efficiently implements OCC with an exponential-size domain, a property which is not even achieved by known constructions in the simpler, synchronous setting. We then turn to the problem of round-preserving parallel composition of asynchronous BA. A protocol for this task was proposed by Ben-Or and El-Yaniv [Distributed Computing '03]. Their construction, however, is flawed in several ways. Thus, as a second contribution, we provide a simpler, more modular protocol for the above task. Finally, and as a contribution of independent interest, we provide proofs in Canetti's Universal Composability framework; this makes our work the first one offering composability guarantees, which are important as BA is a core building block of secure multi-party computation protocols.

cs.CR↗

arXiv2023

The Normal Distributions Indistinguishability Spectrum and its Application to Privacy-Preserving Machine Learning

We investigate the privacy of {\em any} algorithm whose outputs have Gaussian distribution. This work is motivated by the prevalence of such algorithms in several useful (ML) applications, and the comparatively little research that focuses on privacy-preserving learning outside of adding Gaussian noise to the data (such as DP-SGD). {\em What is the DP of any algorithm with multivariate Gaussian output?} We answer the above research question with a general lemma which we call {\em Normal Distributions Indistinguishability Spectrum} (NDIS), a closed-form analytic computation of the hockey-stick divergence $\delta$ between an arbitrary pair of multivariate Gaussians, parameterized by privacy parameter $\epsilon$. To show its practical implications, we prove several properties of our NDIS lemma. These properties form a {\em toolbox} of results which lead to potentially {\em easier} privacy proofs for any Gaussian-output algorithm. As an example application of our toolbox, we prove a tighter parametrisation of the privacy of {\em random projection (RP)}, and obtaining from it a more noise-frugal DP mechanism. Beyond random projection, NDIS can be used to lift {\em any} Gaussian-output algorithm with a `sensitivity' (which we define) to a Gaussian-output DP mechanism. The mechanism boosts the existing randomness in the algorithm, so that one can describe the mechanism's privacy as the IS between a single pair of Gaussians, which can then be analyzed via NDIS. Lastly, we leverage the connections between NDIS and the CDF of the generalized $\chi^2$ distribution (which have efficient empirical estimators) to present a tool for white-box auditing of Gaussian-output algorithms.

cs.CR↗

arXiv2021

How Private Are Commonly-Used Voting Rules?

Differential privacy has been widely applied to provide privacy guarantees by adding random noise to the function output. However, it inevitably fails in many high-stakes voting scenarios, where voting rules are required to be deterministic. In this work, we present the first framework for answering the question: "How private are commonly-used voting rules?" Our answers are two-fold. First, we show that deterministic voting rules provide sufficient privacy in the sense of distributional differential privacy (DDP). We show that assuming the adversarial observer has uncertainty about individual votes, even publishing the histogram of votes achieves good DDP. Second, we introduce the notion of exact privacy to compare the privacy preserved in various commonly-studied voting rules, and obtain dichotomy theorems of exact DDP within a large subset of voting rules called generalized scoring rules.

cs.CR↗

arXiv2019

PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography

Privacy is a major issue in learning from distributed data. Recently the cryptographic literature has provided several tools for this task. However, these tools either reduce the quality/accuracy of the learning algorithm---e.g., by adding noise---or they incur a high performance penalty and/or involve trusting external authorities. We propose a methodology for {\sl private distributed machine learning from light-weight cryptography} (in short, PD-ML-Lite). We apply our methodology to two major ML algorithms, namely non-negative matrix factorization (NMF) and singular value decomposition (SVD). Our resulting protocols are communication optimal, achieve the same accuracy as their non-private counterparts, and satisfy a notion of privacy---which we define---that is both intuitive and measurable. Our approach is to use lightweight cryptographic protocols (secure sum and normalized secure sum) to build learning algorithms rather than wrap complex learning algorithms in a heavy-cost MPC framework. We showcase our algorithms' utility and privacy on several applications: for NMF we consider topic modeling and recommender systems, and for SVD, principal component regression, and low rank approximation.

cs.LG↗