scholarly journals Large Scale, Actively Secure Computation from LPN and Free-XOR Garbled Circuits

2021 ◽  
pp. 33-63
Author(s):  
Aner Ben-Efraim ◽  
Kelong Cong ◽  
Eran Omri ◽  
Emmanuela Orsini ◽  
Nigel P. Smart ◽  
...  
Keyword(s):  
Large Scale ◽  
Secure Computation ◽  
Garbled Circuits

Gradual GRAM and secure computation for RAM programs

2021 ◽  
pp. 1-33
Author(s):  
Carmit Hazay ◽  
Mor Lilintal
Keyword(s):  
Secure Computation ◽  
Boolean Circuits ◽  
Conceptual Similarity ◽  
Garbled Circuits ◽  
Security Guarantees

Despite the fact that the majority of applications encountered in practice today are captured more efficiently by RAM programs, the area of secure two-party computation (2PC) has seen tremendous improvement mostly for Boolean circuits. One of the most studied objects in this domain is garbled circuits. Analogously, garbled RAM (GRAM) provide similar security guarantees for RAM programs with applications to constant round 2PC. In this work we consider the notion of gradual GRAM which requires no memory garbling algorithm. Our approach provides several qualitative advantages over prior works due to the conceptual similarity to the analogue garbling mechanism for Boolean circuits. We next revisit the GRAM construction from (In STOC (2015) 449–458) and improve it in two orthogonal aspects: match it directly with tree-based ORAMs and explore its consistency with gradual ORAM.

scholarly journals SIFO: Secure Computational Infrastructure Using FPGA Overlays

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Xin Fang ◽  
Stratis Ioannidis ◽  
Miriam Leeser
Keyword(s):  
Code Generation ◽  
Heterogeneous Computing ◽  
Computational Cost ◽  
Personal Data ◽  
Secure Computation ◽  
Coarse Grained ◽  
Function Evaluation ◽  
Fpga Design ◽  
Computing Platform ◽  
Garbled Circuits

Secure Function Evaluation (SFE) has received recent attention due to the massive collection and mining of personal data, but remains impractical due to its large computational cost. Garbled Circuits (GC) is a protocol for implementing SFE which can evaluate any function that can be expressed as a Boolean circuit and obtain the result while keeping each party’s input private. Recent advances have led to a surge of garbled circuit implementations in software for a variety of different tasks. However, these implementations are inefficient, and therefore GC is not widely used, especially for large problems. This research investigates, implements, and evaluates secure computation generation using a heterogeneous computing platform featuring FPGAs. We have designed and implemented SIFO: secure computational infrastructure using FPGA overlays. Unlike traditional FPGA design, a coarse-grained overlay architecture is adopted which supports mapping SFE problems that are too large to map to a single FPGA. Host tools provided include SFE problem generator, parser, and automatic host code generation. Our design allows repurposing an FPGA to evaluate different SFE tasks without the need for reprogramming and fully explores the parallelism for any GC problem. Our system demonstrates an order of magnitude speedup compared with an existing software platform.

scholarly journals Publicly verifiable and efficiency/security-adjustable outsourcing scheme for solving large-scale modular system of linear equations

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Panpan Meng ◽  
Chengliang Tian ◽  
Xiangguo Cheng
Keyword(s):  
Coding Theory ◽  
Large Scale ◽  
High Efficiency ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Secure Computation ◽  
Modular System ◽  
System Of Linear Equations ◽  
Public Verifiability ◽  
Cloud Server

AbstractSolving large-scale modular system of linear equations ($\mathcal {LMSLE}$ℒℳSℒE) is pervasive in modern computer and communication community, especially in the fields of coding theory and cryptography. However, it is computationally overloaded for lightweight devices arisen in quantity with the dawn of the things of internet (IoT) era. As an important form of cloud computing services, secure computation outsourcing has become a popular topic. In this paper, we design an efficient outsourcing scheme that enables the resource-constrained client to find a solution of the $\mathcal {LMSLE}$ℒℳSℒE with the assistance of a public cloud server. By utilizing affine transformation based on sparse unimodular matrices, our scheme has three merits compared with previous work: 1) Our scheme is efficiency/security-adjustable. Our encryption method is dynamic, and it can balance the security and efficiency to match different application scenarios by skillfully control the number of unimodular matrices. 2) Our scheme is versatile. It is suit for generic m-by-n coefficient matrix A, no matter it is square or not. 3) Our scheme satisfies public verifiability and achieves the optimal verification probability. It enables any verifier which is not necessarily the client to verify the correctness of the results returned from the cloud server with probability 1. Finally, theoretical analysis and comprehensive experimental results confirm our scheme’s security and high efficiency.

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

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 119 ◽  
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.

scholarly journals Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

2017 ◽  
Vol 2017 (4) ◽  
pp. 345-364 ◽  
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.

SNUSE: A secure computation approach for large-scale user re-enrollment in biometric authentication systems

2019 ◽  
Vol 98 ◽  
pp. 259-273
Author(s):  
Ivan De Oliveira Nunes ◽  
Karim Eldefrawy ◽  
Tancrède Lepoint
Keyword(s):  
Large Scale ◽  
Secure Computation ◽  
Biometric Authentication

scholarly journals Students and Taxes: a Privacy-Preserving Study Using Secure Computation

2016 ◽  
Vol 2016 (3) ◽  
pp. 117-135 ◽  
Author(s):  
Dan Bogdanov ◽  
Liina Kamm ◽  
Baldur Kubo ◽  
Reimo Rebane ◽  
Ville Sokk ◽  
...  
Keyword(s):  
Large Scale ◽  
Applied Research ◽  
Statistical Study ◽  
Real Data ◽  
Privacy Preserving ◽  
Research Data ◽  
Secure Computation ◽  
Ministry Of Education ◽  
Government Data ◽  
Tax Payments

Abstract We describe the use of secure multi-party computation for performing a large-scale privacy-preserving statistical study on real government data. In 2015, statisticians from the Estonian Center of Applied Research (CentAR) conducted a big data study to look for correlations between working during university studies and failing to graduate in time. The study was conducted by linking the database of individual tax payments from the Estonian Tax and Customs Board and the database of higher education events from the Ministry of Education and Research. Data collection, preparation and analysis were conducted using the Share-mind secure multi-party computation system that provided end-to-end cryptographic protection to the analysis. Using ten million tax records and half a million education records in the analysis, this is the largest cryptographically private statistical study ever conducted on real data.

scholarly journals Randomized Oblivious Transfer for Secure Multiparty Computation in the Quantum Setting

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1001
Author(s):  
Bruno Costa ◽  
Pedro Branco ◽  
Manuel Goulão ◽  
Mariano Lemus ◽  
Paulo Mateus
Keyword(s):  
Building Block ◽  
Large Scale ◽  
String Model ◽  
Oblivious Transfer ◽  
Secure Computation ◽  
Multiparty Computation ◽  
Basic Building Block ◽  
Universal Composability ◽  
Common Reference ◽  
Common Reference String

Secure computation is a powerful cryptographic tool that encompasses the evaluation of any multivariate function with arbitrary inputs from mutually distrusting parties. The oblivious transfer primitive serves is a basic building block for the general task of secure multi-party computation. Therefore, analyzing the security in the universal composability framework becomes mandatory when dealing with multi-party computation protocols composed of oblivious transfer subroutines. Furthermore, since the required number of oblivious transfer instances scales with the size of the circuits, oblivious transfer remains as a bottleneck for large-scale multi-party computation implementations. Techniques that allow one to extend a small number of oblivious transfers into a larger one in an efficient way make use of the oblivious transfer variant called randomized oblivious transfer. In this work, we present randomized versions of two known oblivious transfer protocols, one quantum and another post-quantum with ring learning with an error assumption. We then prove their security in the quantum universal composability framework, in a common reference string model.

scholarly journals Privacy-Aware MapReduce Based Multi-Party Secure Skyline Computation

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 207
Author(s):  
Saleh Ahmed ◽  
Mahboob Qaosar ◽  
Asif Zaman ◽  
Md. Anisuzzaman Siddique ◽  
Chen Li ◽  
...  
Keyword(s):  
Data Privacy ◽  
Large Scale ◽  
Communication Complexity ◽  
Cost Effective ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Security And Privacy ◽  
Computation Method ◽  
Computation Model ◽  
Privacy And Security

Selecting representative objects from a large-scale dataset is an important task for understanding the dataset. Skyline is a popular technique for selecting representative objects from a large dataset. It is obvious that the skyline computation from the collective databases of multiple organizations is more effective than the skyline computed from a database of a single organization. However, due to privacy-awareness, every organization is also concerned about the security and privacy of their data. In this regards, we propose an efficient multi-party secure skyline computation method that computes the skyline on encrypted data and preserves the confidentiality of each party’s database objects. Although several distributed skyline computing methods have been proposed, very few of them consider the data privacy and security issues. However, privacy-preserving multi-party skyline computing techniques are not efficient enough. In our proposed method, we present a secure computation model that is more efficient in comparison with existing privacy-preserving multi-party skyline computation models in terms of computation and communication complexity. In our computation model, we also introduce MapReduce as a distributive, scalable, open-source, cost-effective, and reliable framework to handle multi-party data efficiently.

scholarly journals Scalable Secure Privacy-Preserving Record Linkage (PPRL) Methods Using Cloud-based Infrastructure

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Toan Ong ◽  
Ibrahim Lazrig ◽  
Indrajit Ray ◽  
Indrakshi Ray ◽  
Michael Kahn
Keyword(s):  
Parallel Processing ◽  
Record Linkage ◽  
High Capacity ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Third Party ◽  
Major Drawback ◽  
Garbled Circuits ◽  
Chunk Size ◽  
Synthetic Datasets

IntroductionBloom Filters (BFs) are a scalable solution for probabilistic privacy-preserving record linkage but BFs can be compromised. Yao’s garbled circuits (GCs) can perform secure multi-party computation to compute the similarity of two BFs without a trusted third party. The major drawback of using BFs and GCs together is poor efficiency. Objectives and ApproachWe evaluated the feasibility of BFs+GCs using high capacity compute engines and implementing a novel parallel processing framework in Google Cloud Compute Engines (GCCE). In the Yao’s two-party secure computation protocol, one party serves as the generator and the other party serves as the evaluator. To link data in parallel, records from both parties are divided into chunks. Linkage between every two chunks in the same block is processed by a thread. The number of threads for linkage depends on available computing resources. We tested the parallelized process in various scenarios with variations in hardware and software configurations. ResultsTwo synthetic datasets with 10K records were linked using BFs+GCs on 12 different software and hardware configurations which varied by: number of CPU cores (4 to 32), memory size (15GB – 28.8GB), number of threads (6-41), and chunk size (50-200 records). The minimum configuration (4 cores; 15GB memory) took 8,062.4s to complete whereas the maximum configuration (32 cores; 28.8GB memory) took 1,454.1s. Increasing the number of threads or changing the chunk size without providing more CPU cores and memory did not improve the efficiency. Efficiency is improved on average by 39.81% when the number of cores and memory on the both sides are doubled. The CPU utilization is maximized (near 100% on both sides) when the computing power of the generator is double the evaluator. Conclusion/ImplicationsThe PPRL runtime of BFs+GCs was greatly improved using parallel processing in a cloud-based infrastructure. A cluster of GCCEs could be leveraged to reduce the runtime of data linkage operations even further. Scalable cloud-based infrastructures can overcome the trade-off between security and efficiency, allowing computationally complex methods to be implemented.

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