Secure Distributed Storage: Optimal Trade-Off Between Storage Rate and Privacy Leakage

• URL: http://arxiv.org/abs/2403.10676v1
• Date: Fri, 15 Mar 2024 20:50:46 GMT
• Title: Secure Distributed Storage: Optimal Trade-Off Between Storage Rate and Privacy Leakage
• Authors: Remi A. Chou, Joerg Kliewer,
• Abstract summary: We consider the problem of storing data in a distributed manner over $T$ servers. Specifically, the data needs to (i) be recoverable from any $tau$ servers, and (ii) remain private from any $z$ colluding servers. For this model, our main results are (i) the fundamental trade-off between storage size and the level of desired privacy, and (ii) the optimal amount of local randomness necessary at the encoder.
• Score: 1.6881346757176976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Consider the problem of storing data in a distributed manner over $T$ servers. Specifically, the data needs to (i) be recoverable from any $\tau$ servers, and (ii) remain private from any $z$ colluding servers, where privacy is quantified in terms of mutual information between the data and all the information available at any $z$ colluding servers. For this model, our main results are (i) the fundamental trade-off between storage size and the level of desired privacy, and (ii) the optimal amount of local randomness necessary at the encoder. As a byproduct, our results provide an optimal lower bound on the individual share size of ramp secret sharing schemes under a more general leakage symmetry condition than the ones previously considered in the literature.

Related papers

• Federated Transfer Learning with Differential Privacy [21.50525027559563]
  We formulate the notion of textitfederated differential privacy, which offers privacy guarantees for each data set without assuming a trusted central server. We show that federated differential privacy is an intermediate privacy model between the well-established local and central models of differential privacy.
  arXiv  Detail & Related papers  (2024-03-17T21:04:48Z)
• Weakly-Private Information Retrieval From MDS-Coded Distributed Storage [11.955988388140725]
  In WPIR, a user wishes to retrieve a piece of data from a set of servers without leaking too much information about which piece of data she is interested in. We study and provide the first WPIR protocols for this scenario and present results on their optimal trade-off between download rate and information leakage using the maximal leakage privacy metric.
  arXiv  Detail & Related papers  (2024-01-17T18:51:04Z)
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy [51.11280118806893]
  We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP) More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
  arXiv  Detail & Related papers  (2023-02-19T16:58:53Z)
• Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano [83.5933307263932]
  We study data reconstruction attacks for discrete data and analyze it under the framework of hypothesis testing. We show that if the underlying private data takes values from a set of size $M$, then the target privacy parameter $epsilon$ can be $O(log M)$ before the adversary gains significant inferential power.
  arXiv  Detail & Related papers  (2022-10-24T23:50:12Z)
• DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization [58.155151571362914]
  We propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases. splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable privacy budget. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy.
  arXiv  Detail & Related papers  (2022-08-24T17:52:43Z)
• Smooth Anonymity for Sparse Graphs [69.1048938123063]
  differential privacy has emerged as the gold standard of privacy, however, when it comes to sharing sparse datasets. In this work, we consider a variation of $k$-anonymity, which we call smooth-$k$-anonymity, and design simple large-scale algorithms that efficiently provide smooth-$k$-anonymity.
  arXiv  Detail & Related papers  (2022-07-13T17:09:25Z)
• Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent [69.14164921515949]
  We characterize privacy guarantees for individual examples when releasing models trained by DP-SGD. We find that most examples enjoy stronger privacy guarantees than the worst-case bound. This implies groups that are underserved in terms of model utility simultaneously experience weaker privacy guarantees.
  arXiv  Detail & Related papers  (2022-06-06T13:49:37Z)
• Mixed Differential Privacy in Computer Vision [133.68363478737058]
  AdaMix is an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. A few-shot or even zero-shot learning baseline that ignores private data can outperform fine-tuning on a large private dataset.
  arXiv  Detail & Related papers  (2022-03-22T06:15:43Z)
• Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms [9.599356978682108]
  We consider a platform's problem of collecting data from privacy sensitive users to estimate an underlying parameter of interest. We consider two popular differential privacy settings for providing privacy guarantees for the users: central and local. We pose the mechanism design problem as the optimal selection of an estimator and payments that will elicit truthful reporting of users' privacy sensitivities.
  arXiv  Detail & Related papers  (2022-01-10T00:27:43Z)
• Single-Server Private Linear Transformation: The Individual Privacy Case [10.072633952908456]
  This paper considers the single-server Private Linear Transformation (PLT) problem with individual privacy guarantees. The goal is to minimize the download cost while keeping the identity of each message required for the computation individually private.
  arXiv  Detail & Related papers  (2021-06-09T17:12:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.