Privacy-preserving logical vector clocks using secure computation techniques

With the development of social networks, there are more and more social data produced, which usually contain valuable knowledge that can be utilized in many fields, such as commodity recommendation and sentimental analysis. The SVM classifier, as one of the most prevailing machine learning techniques for classification, is a crucial tool for social data analysis. Since training a high-quality SVM classifier usually requires a huge amount of data, it is a better choice for individuals and small enterprises to conduct collaborative training with multiple parties. Nevertheless, it causes privacy risks when sharing sensitive data with untrusted people and enterprises. Existing solutions mainly adopt the computation-intensive cryptographic methods which are not efficient for practical applications. Therefore, it is an urgent and challenging task to realize efficient SVM classifier training while protecting privacy. In this paper, we propose a novel privacy-preserving nonlinear SVM classifier training scheme based on blockchain. We first design a series of secure computation protocols which can achieve secure nonlinear SVM classifier training with minimal computation overheads. Then, leveraging these building blocks, we propose a blockchain-based secure nonlinear SVM classifier training scheme that realizes collaborative training while protecting privacy. We conduct a thorough analysis of the security properties of our scheme. Experiments over a real dataset show that our scheme achieves high accuracy and practical efficiency.

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Secure Computation for Privacy Preserving Data Mining

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch266 ◽

2011 ◽

pp. 1747-1752 ◽

Cited By ~ 1

Author(s):

Yehuda Lindell

Keyword(s):

Data Mining ◽

Privacy Preserving ◽

Secure Computation ◽

Data Mining Algorithm ◽

Privacy Preserving Data Mining ◽

Public And Private ◽

The Public ◽

Use Of Data ◽

To Come ◽

Mining Tools

The increasing use of data mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy preserving data mining solutions achieve the somewhat paradoxical property of enabling a data mining algorithm to use data without ever actually “seeing” it. Thus, the benefits of data mining can be enjoyed, without compromising the privacy of concerned individuals.

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Privacy-preserving policy evaluation in multi-party access control

Journal of Computer Security ◽

10.3233/jcs-200007 ◽

2021 ◽

pp. 1-38

Author(s):

Mina Sheikhalishahi ◽

Ischa Stork ◽

Nicola Zannone

Keyword(s):

Access Control ◽

Homomorphic Encryption ◽

Privacy Preserving ◽

Secure Computation ◽

Sensitive Information ◽

Functional Evaluation ◽

Collaborative Systems ◽

New Paradigm ◽

Access Control Policies ◽

The Individual

Recent years have seen an increasing popularity of online collaborative systems like social networks and web-based collaboration platforms. Collaborative systems typically offer their users a digital environment in which they can work together and share resources and information. These resources and information might be sensitive and, thus, they should be protected from unauthorized accesses. Multi-party access control is emerging as a new paradigm for the protection of co-owned and co-managed resources, where the policies of all users involved in the management of a resource should be accounted for collaborative decision making. Existing approaches, however, only focus on the jointly protection of resources and do not address the protection of the individual user policies themselves, whose disclosure might leak sensitive information. In this work, we propose a privacy-preserving mechanism for the evaluation of multi-party access control policies, which preserves the confidentiality of user policies while remaining capable of making collaborative decisions. To this end, we design secure computation protocols for the evaluation of policies in protected form against an access query and realize such protocols using two privacy-preserving techniques, namely Homomorphic Encryption and Secure Functional Evaluation. We show the practical feasibility of our mechanism in terms of computation and communication costs through an experimental evaluation.

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Privacy-Preserving Search of Similar Patients in Genomic Data

Proceedings on Privacy Enhancing Technologies ◽

10.1515/popets-2018-0034 ◽

2018 ◽

Vol 2018 (4) ◽

pp. 104-124 ◽

Cited By ~ 7

Author(s):

Gilad Asharov ◽

Shai Halevi ◽

Yehuda Lindell ◽

Tal Rabin

Keyword(s):

Approximation Method ◽

Genomic Data ◽

Privacy Preserving ◽

Secure Computation ◽

Great Promise ◽

Full Potential ◽

Genome Data ◽

Similar Patient ◽

Secure Protocols ◽

Private Computation

Abstract The growing availability of genomic data holds great promise for advancing medicine and research, but unlocking its full potential requires adequate methods for protecting the privacy of individuals whose genome data we use. One example of this tension is running Similar Patient Query on remote genomic data: In this setting a doctor that holds the genome of his/her patient may try to find other individuals with “close” genomic data, and use the data of these individuals to help diagnose and find effective treatment for that patient’s conditions. This is clearly a desirable mode of operation. However, the privacy exposure implications are considerable, and so we would like to carry out the above “closeness” computation in a privacy preserving manner. In this work we put forward a new approach for highly efficient secure computation for computing an approximation of the Similar Patient Query problem. We present contributions on two fronts. First, an approximation method that is designed with the goal of achieving efficient private computation. Second, further optimizations of the two-party protocol. Our tests indicate that the approximation method works well, it returns the exact closest records in 98% of the queries and very good approximation otherwise. As for speed, our protocol implementation takes just a few seconds to run on databases with thousands of records, each of length thousands of alleles, and it scales almost linearly with both the database size and the length of the sequences in it. As an example, in the datasets of the recent iDASH competition, after a one-time preprocessing of around 12 seconds, it takes around a second to find the nearest five records to a query, in a size-500 dataset of length- 3500 sequences. This is 2-3 orders of magnitude faster than using state-of-the-art secure protocols with existing edit distance algorithms.

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Privacy-Preserving Secure Computation of Skyline Query in Distributed Multi-Party Databases

Information ◽

10.3390/info10030119 ◽

2019 ◽

Vol 10 (3) ◽

pp. 119 ◽

Cited By ~ 1

Author(s):

Mahboob Qaosar ◽

Asif Zaman ◽

Md. Siddique ◽

Annisa ◽

Yasuhiko Morimoto

Keyword(s):

Big Data ◽

Large Scale ◽

Homomorphic Encryption ◽

Database Systems ◽

Privacy Preserving ◽

Secure Computation ◽

Sensitive Information ◽

Computing Environment ◽

Skyline Query ◽

Attribute Value

Selecting representative objects from a large-scale database is an essential task to understand the database. A skyline query is one of the popular methods for selecting representative objects. It retrieves a set of non-dominated objects. In this paper, we consider a distributed algorithm for computing skyline, which is efficient enough to handle “big data”. We have noticed the importance of “big data” and want to use it. On the other hand, we must take care of its privacy. In conventional distributed algorithms for computing a skyline query, we must disclose the sensitive values of each object of a private database to another for comparison. Therefore, the privacy of the objects is not preserved. However, such disclosures of sensitive information in conventional distributed database systems are not allowed in the modern privacy-aware computing environment. Recently several privacy-preserving skyline computation frameworks have been introduced. However, most of them use computationally expensive secure comparison protocol for comparing homomorphically encrypted data. In this work, we propose a novel and efficient approach for computing the skyline in a secure multi-party computing environment without disclosing the individual attributes’ value of the objects. We use a secure multi-party sorting protocol that uses the homomorphic encryption in the semi-honest adversary model for transforming each attribute value of the objects without changing their order on each attribute. To compute skyline we use the order of the objects on each attribute for comparing the dominance relationship among the objects. The security analysis confirms that the proposed framework can achieve multi-party skyline computation without leaking the sensitive attribute value to others. Besides that, our experimental results also validate the effectiveness and scalability of the proposed privacy-preserving skyline computation framework.

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An efficient secure sum of multi-scalar products protocol base on elliptic curve

Journal of Science and Technology on Information security ◽

10.54654/isj.v14i2.196 ◽

2022 ◽

Vol 2 (14) ◽

pp. 18-25

Author(s):

Vu Thi Van ◽

Luong The Dung ◽

Hoang Van Quan ◽

Tran Thi Luong

Keyword(s):

Elliptic Curve ◽

Elliptic Curve Cryptography ◽

Scalar Product ◽

Recommendation System ◽

Statistical Data ◽

Privacy Preserving ◽

Secure Computation ◽

Statistical Data Analysis ◽

Privacy Preserving Data Mining ◽

Scalar Products

The secure scalar product protocol is widely applied to solve practical problems such as privacy-preserving data mining, secure auction, secure electronic voting, privacy-preserving recommendation system, privacy-preserving statistical data analysis, etc.. In this paper, we propose an efficient multi-party secure computation protocol using Elliptic curve cryptography, which allows to compute the sum value of multi-scalar products without revealing about the input vectors. Moreover, theoretical and experimental analysis shows that the proposed method is more efficient than others in both computation and communication.

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From Centralized Protection to Distributed Edge Collaboration: A Location Difference-Based Privacy-Preserving Framework for Mobile Crowdsensing

Security and Communication Networks ◽

10.1155/2021/5855745 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Zihao Shao ◽

Huiqiang Wang ◽

Yifan Zou ◽

Zihan Gao ◽

Hongwu Lv

Keyword(s):

Location Privacy ◽

Privacy Preserving ◽

Secure Computation ◽

User Characteristics ◽

User Privacy ◽

Mobile Crowdsensing ◽

Data Coding ◽

Sensing Platform ◽

Single Region ◽

The Impact

Mobile Crowdsensing (MCS) has evolved into an effective and valuable paradigm to engage mobile users to sense and collect urban-scale information. However, users risk their location privacy while reporting data with actual sensing locations. Existing works of location privacy-preserving are primarily based on single-region location information, which rely on a trusted and centralized sensing platform and ignore the impact of regional differences on user privacy-preserving demands. To tackle this issue, we propose a Location Difference-Based Privacy-Preserving Framework (LDPF), leveraging the powerful edge servers deployed between users and the sensing platform to hide and manage users according to regional user characteristics. More specifically, for popular regions, based on the edge servers and the k-anonymity algorithm, we propose a Coordinate Transformation and Bit Commitment (CTBC) privacy-preserving method that effectively guarantees the privacy of location data without relying on a trusted sensing platform. For remote regions, based on a more realistic distance calculation mode, we design a Paillier Encryption Data Coding (PDC) privacy-preserving method that realizes the secure computation for users’ location and prevents malicious users from deceiving. The theoretical analysis and simulation results demonstrate the security and efficiency of the proposed framework in location difference-based privacy-preserving.

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Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

Proceedings on Privacy Enhancing Technologies ◽

10.1515/popets-2017-0053 ◽

2017 ◽

Vol 2017 (4) ◽

pp. 345-364 ◽

Cited By ~ 27

Author(s):

Adrià Gascón ◽

Phillipp Schoppmann ◽

Borja Balle ◽

Mariana Raykova ◽

Jack Doerner ◽

...

Keyword(s):

Linear Regression ◽

Convergence Rates ◽

Comprehensive Evaluation ◽

Linear Equations ◽

Privacy Preserving ◽

Secure Computation ◽

Training Dataset ◽

Linear Regression Models ◽

Garbled Circuits ◽

Point Representation

Abstract We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD) algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013), and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.

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