Private Information Retrieval for Secure Distributed Storage Systems

Yang, Heecheol; Shin, Wonjae; Lee, Jung Woo

doi:10.1109/tifs.2018.2833050

Cited by 85 publications

(39 citation statements)

References 24 publications

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“…Note that this expression matches the achievable rate bound of [31]. However, it is strictly smaller than, 1 − 2/N , the asymptotic capacity of XSTPIR for this setting.…”

Section: Alignment Of Noise and Interferencesupporting

confidence: 66%

“…However, it is strictly smaller than, 1 − 2/N , the asymptotic capacity of XSTPIR for this setting. Evidently, the natural MDS-PIR solution, and the secret sharing based scheme of [31], are asymptotically suboptimal. In fact, the MDS-PIR solution falls short of the asymptotic (K → ∞) capacity of XSTPIR, even if the MDS-PIR scheme is only required to deal with K = 2 messages.…”

Section: Alignment Of Noise and Interferencementioning

confidence: 99%

See 1 more Smart Citation

Cross Subspace Alignment and the Asymptotic Capacity of $X$ -Secure $T$ -Private Information Retrieval

Jia

Sun

Jafar

2019

IEEE Trans. Inform. Theory

104

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X-secure and T -private information retrieval (XSTPIR) is a form of private information retrieval where data security is guaranteed against collusion among up to X servers and the user's privacy is guaranteed against collusion among up to T servers. The capacity of XSTPIR is characterized for arbitrary number of servers N , and arbitrary security and privacy thresholds X and T , in the limit as the number of messages K → ∞. Capacity is also characterized for any number of messages if either N = 3, X = T = 1 or if N ≤ X + T . Insights are drawn from these results, about aligning versus decoding noise, dependence of PIR rate on field size, and robustness to symmetric security constraints. In particular, the idea of cross subspace alignment, i.e., introducing a subspace dependence between Reed-Solomon code parameters, emerges as the optimal way to align undesired terms while keeping desired terms resolvable.

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“…Note that this expression matches the achievable rate bound of [31]. However, it is strictly smaller than, 1 − 2/N , the asymptotic capacity of XSTPIR for this setting.…”

Section: Alignment Of Noise and Interferencesupporting

confidence: 66%

Section: Alignment Of Noise and Interferencementioning

confidence: 99%

Cross Subspace Alignment and the Asymptotic Capacity of $X$ -Secure $T$ -Private Information Retrieval

Jia

Sun

Jafar

2019

IEEE Trans. Inform. Theory

104

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show abstract

“…In order to execute queries and compute statistics on distributed datasets, multiple decentralized solutions [10], [12], [14], [22], [23], [24], [25] rely on techniques that have a high expressive power, such as secret sharing and garbled circuits. These solutions are often flexible in the computations they offer but usually assume (a) honest-but-curious computing parties and (b) no collusion or a 2-party model.…”

Section: Related Workmentioning

confidence: 99%

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

Froelicher

Troncoso-Pastoriza

Sousa

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditional consent or is often simply forbidden for privacy and security reasons. In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets. Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evaluation of machine-learning models on sensitive and distributed data. To ensure data confidentiality and the privacy of the data providers, Drynx combines interactive protocols, homomorphic encryption, zero-knowledge proofs of correctness, and differential privacy. It enables an efficient and decentralized verification of the input data and of all the system's computations thus provides auditability in a strong adversarial model in which no entity has to be individually trusted. Drynx is highly modular, dynamic and parallelizable. Our evaluation shows that it enables the training of a logistic regression model on a dataset (12 features and 600,000 records) distributed among 12 data providers in less than 2 seconds. The computations are distributed among 6 computing nodes, and Drynx enables the verification of the query execution's correctness in less than 22 seconds.

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“…Regarding future work, one interesting direction is to investigate the information-theoretic limits of the problem where the eavesdropper can also tap in on the storage at the servers (schemes are provided in a recent work [23]). The other promising direction is to explore the T -colluding PIR problem with a combination adversary who can both eavesdrop and corrupt communications.…”

Section: Discussionmentioning

confidence: 99%

On PIR and Symmetric PIR From Colluding Databases With Adversaries and Eavesdroppers

Wang

Skoglund

2019

IEEE Trans. Inform. Theory

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We consider the problem of private information retrieval (PIR) and symmetric private information retrieval (SPIR) from replicated databases with colluding servers, in the presence of Byzantine adversaries and eavesdroppers. Specifically, there are K messages replicatively stored at N databases. A user wants to retrieve one message by communicating with the databases, without revealing the identity of the message retrieved. For T -colluding databases, any T out of N databases may communicate their interactions with the user to guess the identity of the requested message. We consider the situation where the communication system can be vulnerable to attachers, namely, there is an adversary in the system that can tap in on or even try to corrupt the communication. The capacity is defined as the maximum number of information bits of the desired message retrieved per downloaded bit. For SPIR, it is further required that the user learns nothing about the other K − 1 messages in the database. Three types of adversaries are considered: a Byzantine adversary who can overwrite the transmission of any B servers to the user; a passive eavesdropper who can tap in on the incoming and outgoing transmissions of any E servers; and a combination of both -an adversary who can tap in on a set of any E nodes, and overwrite the transmission of a set of any B nodes. The problems of SPIR with colluding servers and the three types of adversaries are named T-BSPIR, T-ESPIR and T-BESPIR respectively. We derive the capacities of the three secure SPIR problems. The results resemble those of secure network coding problems with adversaries and eavesdroppers. The capacity of T -colluding PIR with Byzantine adversaries is characterized in [1]. In this work, we consider T -colluding PIR with an eavedropper (named T-EPIR). We derive the T-EPIR capacity when E ≥ T ; for the case where E ≤ T , we find an outer bound (converse bound) and an inner bound (achievability) on the optimal achievable rate. I. INTRODUCTIONWith the evolution of the Internet of things, big data, cloud storage and computing, much of the emerging technology relies on open systems and networks, which brings up new challenges in preserving data privacy and security in such applications. Recently, various privacy and security problems in the storage, fetching, and communication of data have been studied using tools from information theory, among which the problem of private information retrieval brought up in the 90s Manuscript

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Private Information Retrieval for Secure Distributed Storage Systems

Cited by 85 publications

References 24 publications

Cross Subspace Alignment and the Asymptotic Capacity of $X$ -Secure $T$ -Private Information Retrieval

Cross Subspace Alignment and the Asymptotic Capacity of $X$ -Secure $T$ -Private Information Retrieval

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

On PIR and Symmetric PIR From Colluding Databases With Adversaries and Eavesdroppers

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