arXiv subjects

Sahel Torkamani

Publications and source records attributed to Sahel Torkamani.

5 recordsLinked to original sources

arXiv2026

PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces

Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model builders and data providers are often unwilling to reveal their proprietary assets. We present PrivaDE, a privacy-preserving protocol that allows a model owner and a data owner to jointly compute a utility score for a candidate dataset without fully exposing model parameters, raw features, or labels. PrivaDE provides strong security against malicious behavior and can be integrated into blockchain-based marketplaces, where smart contracts enforce fair execution and payment. To make the protocol practical, we propose optimizations to enable efficient secure model inference, and a model-agnostic scoring method that uses only a small, representative subset of the data while still reflecting its impact on downstream training. Evaluation shows that PrivaDE performs data evaluation effectively, achieving online runtimes within 15 minutes even for models with millions of parameters. Our work lays the foundation for fair and automated data marketplaces in decentralized machine learning ecosystems.

cs.CR↗

arXiv2026

Magnitude Distance: A Geometric Measure of Dataset Similarity

Quantifying the distance between datasets is a fundamental question in mathematics and machine learning. We propose \textit{magnitude distance}, a novel distance metric defined on finite datasets using the notion of the \emph{magnitude} of a metric space. The proposed distance incorporates a tunable scaling parameter, $t$, that controls the sensitivity to global structure (small $t$) and finer details (large $t$). We prove several theoretical properties of magnitude distance, including its limiting behavior across scales and conditions under which it satisfies key metric properties. In contrast to classical distances, we show that magnitude distance remains discriminative in high-dimensional settings when the scale is appropriately tuned. We further demonstrate how magnitude distance can be used as a training objective for push-forward generative models. Our experimental results support our theoretical analysis and demonstrate that magnitude distance provides meaningful signals, comparable to established distance-based generative approaches.

cs.LG↗

arXiv2024

Count on Your Elders: Laplace vs Gaussian Noise

In recent years, Gaussian noise has become a popular tool in differentially private algorithms, often replacing Laplace noise which dominated the early literature. Gaussian noise is the standard approach to $\textit{approximate}$ differential privacy, often resulting in much higher utility than traditional (pure) differential privacy mechanisms. In this paper we argue that Laplace noise may in fact be preferable to Gaussian noise in many settings, in particular for $(\varepsilon,δ)$-differential privacy when $δ$ is small. We consider two scenarios: First, we consider the problem of counting under continual observation and present a new generalization of the binary tree mechanism that uses a $k$-ary number system with $\textit{negative digits}$ to improve the privacy-accuracy trade-off. Our mechanism uses Laplace noise and whenever $δ$ is sufficiently small it improves the mean squared error over the best possible $(\varepsilon,δ)$-differentially private factorization mechanisms based on Gaussian noise. Specifically, using $k=19$ we get an asymptotic improvement over the bound given in the work by Henzinger, Upadhyay and Upadhyay (SODA 2023) when $δ= O(T^{-0.92})$. Second, we show that the noise added by the Gaussian mechanism can always be replaced by Laplace noise of comparable variance for the same $(ε, δ)$-differential privacy guarantee, and in fact for sufficiently small $δ$ the variance of the Laplace noise becomes strictly better. This challenges the conventional wisdom that Gaussian noise should be used for high-dimensional noise. Finally, we study whether counting under continual observation may be easier in an average-case sense. We show that, under pure differential privacy, the expected worst-case error for a random input must be $Ω(\log(T)/\varepsilon)$, matching the known lower bound for worst-case inputs.

cs.CR↗

arXiv2023

Optimal Binary Differential Privacy via Graphs

We present the notion of \emph{reasonable utility} for binary mechanisms, which applies to all utility functions in the literature. This notion induces a partial ordering on the performance of all binary differentially private (DP) mechanisms. DP mechanisms that are maximal elements of this ordering are optimal DP mechanisms for every reasonable utility. By looking at differential privacy as a randomized graph coloring, we characterize these optimal DP in terms of their behavior on a certain subset of the boundary datasets we call a boundary hitting set. In the process of establishing our results, we also introduce a useful notion that generalizes DP conditions for binary-valued queries, which we coin as suitable pairs. Suitable pairs abstract away the algebraic roles of $\varepsilon,δ$ in the DP framework, making the derivations and understanding of our proofs simpler. Additionally, the notion of a suitable pair can potentially capture privacy conditions in frameworks other than DP and may be of independent interest.

cs.IT↗

arXiv2022

Heterogeneous Differential Privacy via Graphs

We generalize a previous framework for designing utility-optimal differentially private (DP) mechanisms via graphs, where datasets are vertices in the graph and edges represent dataset neighborhood. The boundary set contains datasets where an individual's response changes the binary-valued query compared to its neighbors. Previous work was limited to the homogeneous case where the privacy parameter $\varepsilon$ across all datasets was the same and the mechanism at boundary datasets was identical. In our work, the mechanism can take different distributions at the boundary and the privacy parameter $\varepsilon$ is a function of neighboring datasets, which recovers an earlier definition of personalized DP as special case. The problem is how to extend the mechanism, which is only defined at the boundary set, to other datasets in the graph in a computationally efficient and utility optimal manner. Using the concept of strongest induced DP condition we solve this problem efficiently in polynomial time (in the size of the graph).

cs.DS↗