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30th ACM SIGSAC Conference on Computer and Communications Security (ACM CCS)

作者： Kacsmar, Bailey Duddu, Vasisht Tilbury, Kyle Ur, Blase Kerschbaum, Florian Univ Alberta Edmonton AB Canada Univ Waterloo Waterloo ON Canada Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9798400700507

private computation, which includes techniques like multi-party computation and private query execution, holds great promise for enabling organizations to analyze data they and their partners hold while maintaining data subjects' privacy. Despite recent interest in communicating about differential privacy, end users' perspectives on private computation have not previously been studied. To fill this gap, we conducted 22 semi-structured interviews investigating users' understanding of, and expectations for, private computation over data about them. Interviews centered on four concrete data-analysis scenarios (e.g., ad conversion analysis), each with a variant that did not use private computation and another that did. While participants struggled with abstract definitions of private computation, they found the concrete scenarios enlightening and plausible even though we did not explain the complex cryptographic underpinnings. private computation increased participants' acceptance of data sharing, but not unconditionally;the purpose of data sharing and analysis was the primary driver of their attitudes. Through collective activities, participants emphasized the importance of detailing the purpose of a computation and clarifying that inputs to private computation are not shared across organizations when describing private computation to end users.

关键词： private computation Secure Multi-Party computation User Study

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Fundamental Limits of Multi-Message private computation

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IEEE Transactions on Communications 2025年

作者： Gholami, Ali Wan, Kai Jahani-Nezhad, Tayyebeh Sun, Hua Ji, Mingyue Caire, Giuseppe The Electrical Engineering and Computer Science Department Technische Universität Berlin Berlin10587 Germany The School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical Engineering University of North Texas DentonTX76203 United States The Department of Electrical and Computer Engineering the University of Florida GainesvilleFL32611 United States

In a typical formulation of the private information retrieval (PIR) problem, a single user wishes to retrieve one out of K files from N servers without revealing the demanded file index to any server. This paper formulates an extended model of PIR, referred to as multi-message private computation (MMPC), where instead of retrieving a single file, the user wishes to retrieve P > 1 linear combinations of files while preserving the privacy of the demand information. The MM-PC problem is a generalization of the private computation (PC) problem (where the user requests one linear combination of the files), and the multi-message private information retrieval (MM-PIR) problem (where the user requests P > 1 files). A baseline achievable scheme repeats the optimal PC scheme by Sun and Jafar P times, or treats each possible demanded linear combination as an independent file and then uses the near optimal MM-PIR scheme by Banawan and Ulukus. In this paper, we propose a new MMPC scheme that significantly improves upon the baseline schemes. In doing so, we design the queries inspired by the structure in the cache-aided scalar linear function retrieval scheme by Wan et al., which leverages the dependency between linear functions to reduce the amount of communications. To ensure the decodability of our scheme, we propose a new method to benefit from the existing dependency, referred to as the sign assignment step. In the end, we use Maximum Distance Separable matrices to code the queries, which allows the reduction of download from the servers, while preserving privacy. By the proposed schemes, we characterize the capacity within a multiplicative factor of 2. © 1972-2012 IEEE.

关键词： multi-message private information retrieval multiple linear combinations private computation

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On the Communication-computation Tradeoff for Symmetric private Linear computation

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第6期71卷 4284-4305页

作者： Zhu, Jinbao Tang, Xiaohu Southwest Jiaotong Univ Informat Coding & Transmiss ICT Key Lab Sichuan Pr Chengdu 611756 Peoples R China

We consider the problem of symmetric private linear computation (SPLC) over a replicated storage system with colluding and straggler constraints. The SPLC problem allows the user to privately compute a linear combination of multiple files from a set of replicated servers, even in the presence of straggler servers that can bottleneck the entire computation. It is guaranteed that a certain number of colluding servers learn nothing about the coefficients of the linear combination, and the user must not learn any information about the files other than the desired linear computation. Unlike previous private computation literature that mainly focused on decreasing download cost from servers, we aim to establish a flexible tradeoff between communication costs and computational complexities. In particular, we propose a novel SPLC scheme under the assumption of a fixed number of stragglers. Additionally, by generalizing this SPLC scheme, we construct an adaptive SPLC scheme capable of tolerating the presence of a varying number of stragglers, even if their identities and numbers are unknown in advance. Compared to the SPLC scheme with a fixed number of stragglers, the adaptive SPLC scheme achieves a lower communication cost based on the actual number of stragglers, albeit at the cost of increased computational complexities. Both types of SPLC schemes achieve flexible performance tradeoffs and can be employed to optimize system efficiency in practice.

关键词： Servers Costs Polynomials Privacy computational complexity Decoding Interpolation Training Sun Random variables private computation symmetric private linear computation communication and computation stragglers

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private Linear computation for Noncolluding Coded Databases

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2022年第3期40卷 847-861页

作者： Obead, Sarah A. Lin, Hsuan-Yin Rosnes, Eirik Kliewer, Jorg New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA Simula UiB N-5006 Bergen Norway

private computation in a distributed storage system (DSS) is a generalization of the private information retrieval (PIR) problem. In such a setting, a user wishes to compute a function of f messages stored in n noncolluding coded databases, i.e., databases storing data encoded with an [n,k] linear storage code, while revealing no information about the desired function to the databases. We consider the problem of private linear computation (PLC) for coded databases. In PLC, a user wishes to compute a linear combination over the f messages while keeping the coefficients of the desired linear combination hidden from the databases. For a DSS setup where data is stored using a code from a particular family of linear storage codes, we derive an outer bound on the PLC rate, which is defined as the ratio of the desired amount of information and the total amount of downloaded information. In particular, the proposed converse is valid for any number of messages and linear combinations, and depends on the rank of the coefficient matrix obtained from all linear combinations. Further, we present a PLC scheme with rate equal to the outer bound and hence settle the PLC capacity for the considered class of linear storage codes. Interestingly, the PLC capacity matches the maximum distance separable coded capacity of PIR for the considered class of linear storage codes.

关键词： Spread spectrum communication Codes Protocols Distributed databases Information retrieval Indexes Linear codes Capacity information-theoretic privacy private computation private information retrieval

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Symmetric private Polynomial computation From Lagrange Encoding

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第4期68卷 2704-2718页

作者： Zhu, Jinbao Yan, Qifa Tang, Xiaohu Li, Songze Southwest Jiaotong Univ Informat Coding & Transmiss Key Lab Sichuan Prov CSNMT Int Cooperat Res Ctr Most Chengdu 611756 Peoples R China Hong Kong Univ Sci & Technol Thrust Internet Things Guangzhou 511458 Peoples R China

The problem of X-secure T-colluding symmetric private Polynomial computation (PPC) from coded storage system with B Byzantine and U unresponsive servers is studied in this paper. Specifically, a dataset consisting of M files is stored across N distributed servers according to (N, K + X) Maximum Distance Separable (MDS) codes such that any group of up to X colluding servers can not learn anything about the data files. A user wishes to privately evaluate one out of a set of candidate polynomial functions over the M files from the system, while guaranteeing that any T colluding servers can not learn anything about the identity of the desired function and the user can not learn anything about the M data files more than the desired polynomial function evaluations, in the presence of B Byzantine servers that can send arbitrary responses maliciously to confuse the user and U unresponsive servers that will not respond any information at all. A novel symmetric PPC scheme using Lagrange encoding is proposed. This scheme achieves a PPC rate of 1 - G(K+X-1)+T +2B/N-U with secrecy rate G(K+X-1)+T/N-(G(K+X-1)+T +2B+U) and finite field size N + max{K, N-( G(K+X-1)+ T +2B U)}, where G is the maximum degree over all the candidate polynomial functions. Moreover, to further measure the efficiency of PPC schemes, upload cost, query complexity, server computation complexity and decoding complexity required to implement the scheme are analyzed. Remarkably, the PPC setup studied in this paper generalizes all the previous MDS coded PPC setups and the degraded schemes strictly outperform the best known schemes in terms of (asymptotical) PPC rate, which is the main concern of the PPC schemes.

关键词： private computation symmetric private polynomial computation Lagrange encoding computation complexity

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The Capacity of private computation

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第6期65卷 3880-3897页

作者： Sun, Hua Jafar, Syed Ali Univ North Texas Dept Elect Engn Denton TX 76203 USA Univ Calif Irvine Dept Elect Engn & Comp Sci Ctr Pervas Commun & Comp Irvine CA 92697 USA

We introduce the problem of private computation, comprised of N distributed and non-colluding servers, K independent datasets, and a user who wants to compute a function of the datasets privately, i.e., without revealing which function he wants to compute, to any individual server. This private computation problem is a strict generalization of the private information retrieval (PIR) problem, obtained by expanding the PIR message set (which consists of only independent messages) to also include functions of those messages. The capacity of private computation, C, is defined as the maximum number of bits of the desired function that can be retrieved per bit of total download from all servers. We characterize the capacity of private computation, for N servers and K independent datasets that are replicated at each server, when the functions to be computed are arbitrary linear combinations of the datasets. Surprisingly, the capacity, C = (1 + 1/N + ... + 1/NK-1)(-1), matches the capacity of PIR with N servers and K messages. Thus, allowing arbitrary linear computations does not reduce the communication rate compared to pure dataset retrieval. The same insight is shown to hold even for arbitrary non-linear computations when the number of datasets K -> infinity.

关键词： Capacity private computation private information retrieval

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private Polynomial computation From Lagrange Encoding

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2020年 15卷 553-563页

作者： Raviv, Netanel Karpuk, David A. CALTECH Dept Elect Engn Pasadena CA 91125 USA Univ Los Andes Dept Matemat Bogota 111711 Colombia

private computation is a generalization of private information retrieval, in which a user is able to compute a function on a distributed dataset without revealing the identity of that function to the servers. In this paper, it is shown that Lagrange encoding, a powerful technique for encoding Reed-Solomon codes, enables private computation in many cases of interest. In particular, we present a scheme that enables private computation of polynomials of any degree on Lagrange encoded data, while being robust to Byzantine and straggling servers, and to servers colluding to attempt to deduce the identities of the functions to be evaluated. Moreover, incorporating ideas from the well-known Shamir secret sharing scheme allows the data itself to be concealed from the servers as well. Our results extend private computation to high degree polynomials and to data-privacy, and reveal a tight connection between private computation and coded computation.

关键词： private computation private information retrieval (PIR) data privacy Reed-Solomon codes

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private Function computation

Private Function Computation

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IEEE International Symposium on Information Theory (ISIT)

作者： Tahmasebi, Behrooz Maddah-Ali, Mohammad Ali MIT EECS Dept 77 Massachusetts Ave Cambridge MA 02139 USA Sharif Univ Technol EE Dept Tehran Iran

ISBN: (纸本)9781728164328

In this paper, we study the problem of private function computation, where a user wants to compute a function of some inputs, using N \in \mathbb{N} servers, where the function is a private combination/composition of some K \in \mathbb{N} public basic functions {f 1 , f 2 ,., f K }. More precisely, for some inputs W m , m [1 : M], the user's goal is to calculate h\left( {{W-m}} \right) = \sum\nolimits-{j = 1}^J {{\alpha -j}} {h-j}\left( {{W-m}} \right), for some J \in \mathbb{N}, some scalers α j , j [1 : J], and some functions h j (.), j [1 : J], where each is an arbitrary compositions of the basic functions {f 1 , f 2 ,., f K }. The computation is done through a sequence of queries to N servers. In each query, the user sends an input W , which is a (possibly randomized) function of W 1:M and the answers to the previous queries, to one of the servers, and asks the server to return f k (W ), for some k [1 : K]. The servers should not obtain any information about the structure of the function h(.), i.e., the way the basic functions are combined to form h(.), from the sequence of queries they received, even if T of them collude, for some T \in \mathbb{N}.In this paper, we focus on the cases, where basic functions are linear and can be represented by (possibly large-scale) full-rank matrices, and each basic function may contribute in function h(.) for at most once. We prove that C, defined as the supremum of the number of desired computations of the basic functions, normalized by the number of queries, in asymptotic regimes of large M, satisfies the following inequality:\begin{equation∗}\min \left\{ {\left( {1 - \frac{T}{N}} \right)/\left( {1 - \fraccomputation{K}} \right),\left( {1 - \frac{{T - 1}}{N}} \right)} \right\} \leq C \leq 1.\end{equation∗}The key idea is that in the proposed scheme, each server is asked to compute a specific order of basic functions, independent from the user's desired function. In addition, some random vectors are added to the inputs of the queries

关键词： private computation private function retrieval private information retrieval

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private Polynomial Function computation for Noncolluding Coded Databases

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2022年 17卷 1800-1813页

作者： Obead, Sarah A. Lin, Hsuan-Yin Rosnes, Eirik Kliewer, Jorg New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA Simula UiB N-5006 Bergen Norway

We consider the problem of private polynomial computation (PPC) from a distributed storage system (DSS). In such setting a user wishes to compute a multivariate polynomial of degree at most g over f variables (or messages) stored in n noncolluding coded databases, i.e., databases storing data encoded with an [n, k] linear storage code, while revealing no information about the desired polynomial evaluation to the databases. For a DSS setup where data is stored using linear storage codes, we derive an outer bound on the PPC rate, which is defined as the ratio of the (minimum) desired amount of information and the total amount of downloaded information, and construct two novel PPC schemes. In the first scheme, we consider Reed-Solomon coded databases with Lagrange encoding, which leverages ideas from recently proposed star-product private information retrieval and Lagrange coded computation. The second scheme considers the special case of coded databases with systematic Lagrange encoding. Both schemes yield improved rates, while asymptotically, as f -> infinity, the systematic scheme gives a significantly better computation retrieval rate compared to all known schemes up to some storage code rate that depends on the maximum degree of the candidate polynomials.

关键词： Coded computation information-theoretic privacy private computation private information retrieval Reed-Solomon codes

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Towards Real-World private computations with Homomorphic Encryption: Current Solutions and Open Challenges

Towards Real-World Private Computations with Homomorphic Enc...

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28th European Symposium on Research in Computer Security (ESORICS)

作者： Iezzi, Michela Maple, Carsten Leonetti, Andrea Banca Italia Rome Italy Alan Turing Inst London England

ISBN: (纸本)9783031542039;9783031542046

There is an increasing need to share sensitive information within and beyond organisations. Protecting this information is vital for commercial and regulatory reasons. Homomorphic Encryption (HE) has come to the fore as a mechanism to enable the sharing of confidential data in a secure and private manner. Multiple open-source libraries are now publicly available, providing organisations with the tools to utilise the advantages of HE. While research devoted much effort to the academic and cryptographic aspects of HE schemes, research explicitly focusing on real-world financial applications is comparably rare. There is a need to provide a comparative analysis and related benchmarking of the most suitable HE libraries, having fixed the functional and non-functional requirements of the enterprise application of interest. We consider the motivation and background for HE and discuss the most promising opensource HE libraries. Having introduced real-world use cases in a financial context, we then illustrate outstanding challenges and how we plan to circumvent open points, introducing HELT (Homomorphic Encryption Libraries Toolkit).

关键词： Homomorphic Encryption private computation Real-world applications

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