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Sharemind: A Framework for Fast Privacy-Preserving Computations [chapter]

Dan Bogdanov, Sven Laur, Jan Willemson
2008 Lecture Notes in Computer Science  
Our solution-SHAREMIND-is a virtual machine for privacy-preserving data processing that relies on share computing techniques.  ...  This is a standard way for securely evaluating functions in a multi-party computation environment.  ...  Hence, we focused mainly on practical aspects and developed the SHAREMIND framework for privacy-preserving computations.  ... 
doi:10.1007/978-3-540-88313-5_13 fatcat:72qcl5f2cjbjbe6rbdwnq5b6uq

A Universal Toolkit for Cryptographically Secure Privacy-Preserving Data Mining [chapter]

Dan Bogdanov, Roman Jagomägis, Sven Laur
2012 Lecture Notes in Computer Science  
We list the building blocks needed to deploy a privacy-preserving data mining application and explain the design decisions that make Sharemind applications efficient in practice.  ...  Furthermore, there are no established tools for applying secure multi-party computation in real-world applications.  ...  Therefore, a data mining expert does not have to be a cryptography expert to use SHAREMIND and SECREC for creating privacy-preserving data mining applications.  ... 
doi:10.1007/978-3-642-30428-6_9 fatcat:xnatn2zo4ffythvc3gwmw4aype

A Secure Genetic Algorithm for the Subset Cover Problem and Its Application to Privacy Protection [chapter]

Dan Bogdanov, Keita Emura, Roman Jagomägis, Akira Kanaoka, Shin'ichiro Matsuo, Jan Willemson
2014 Lecture Notes in Computer Science  
Performance tests show that our privacy-preserving implementation achieves a 99.32% precision within 6.5 seconds on a BIP problem of moderate size.  ...  We implement and benchmark our solution on the Sharemind secure computation system.  ...  This research has been supported by the European Regional Development Fund through the Estonian Center of Excellence in Computer Science (EXCS), UaESMC project financed by the EU 7th Framework Programme  ... 
doi:10.1007/978-3-662-43826-8_8 fatcat:55r4uocvufagtaefb2tyerqume

Privacy-Preserving Statistical Data Analysis on Federated Databases [chapter]

Dan Bogdanov, Liina Kamm, Sven Laur, Pille Pruulmann-Vengerfeldt, Riivo Talviste, Jan Willemson
2014 Lecture Notes in Computer Science  
In this paper, we propose a novel way to combine secure multi-party computation technology with federated database systems to preserve privacy in statistical studies that combine and analyse data from  ...  We describe an implementation on two real-world platforms-the Sharemind secure multi-party computation and the X-Road database federation platform.  ...  Acknowledgements This research was supported by the European Regional Development The authors wish to thank the interviewees for their time and cooperation and the Estonian Center for Applied Research  ... 
doi:10.1007/978-3-319-06749-0_3 fatcat:uvfasioddjfgflnn3px2xw54nu

AMPPERE: A Universal Abstract Machine for Privacy-Preserving Entity Resolution Evaluation [article]

Yixiang Yao, Tanmay Ghai, Srivatsan Ravi, Pedro Szekely
2021 arXiv   pre-print
We propose AMMPERE, an abstract computation model for performing universal privacy-preserving entity resolution.  ...  We rigorously compare and analyze the feasibility, performance overhead and privacy-preserving properties of these approaches on the Sharemind multi-party computation (MPC) platform as well as on PALISADE  ...  Firstly, we note that AMPPERE provides cryptographic protections for privacy-preservation: the MPC-based implementation provides privacy of computation to all parties subject to a majority being corrupted  ... 
arXiv:2108.09879v1 fatcat:twsrvccqsjhzxltu7o5kmsq5n4

The Present and Future of Privacy-Preserving Computation in Fog Computing [chapter]

Patrícia R. Sousa, Luís Antunes, Rolando Martins
2017 Fog Computing in the Internet of Things  
Given this state-of-affairs, we decided to pursuit an overview and future directions for novel approaches for privacy-preserving computation.  ...  Preface The ever-increasing pervasiveness of edge computing is creating challenges for users' privacy.  ...  • Secure communication using SSL. [2] Sharemind Sharemind is a framework for privacy-preserving computations.  ... 
doi:10.1007/978-3-319-57639-8_4 fatcat:gqzs5iek5zgtzadpjncyej6tpm

Domain-polymorphic language for privacy-preserving applications

Dan Bogdanov, Peeter Laud, Jaak Randmets
2013 Proceedings of the First ACM workshop on Language support for privacy-enhancing technologies - PETShop '13  
We present SecreC, a programming language for specifying privacy-preserving applications using a mix of techniques for secure multiparty computation.  ...  We have implemented the compiler for the language, integrated it with the existing SMC framework Sharemind, and are currently using it for new privacypreserving applications.  ...  ACKNOWLEDGEMENTS This work was supported by the European Social Fund through the ICT Doctoral School programme, and by the European Regional Development Fund through the Estonian Center of Excellence in Computer  ... 
doi:10.1145/2517872.2517875 dblp:conf/ccs/BogdanovLR13 fatcat:wkjmnv4cvfcfnjqgt66ihqggg4

Privacy-preserving record linkage in large databases using secure multiparty computation

Peeter Laud, Alisa Pankova
2018 BMC Medical Genomics  
This paper presents a solution to privacy-preserving deduplication among records of several databases using secure multiparty computation.  ...  This task is very closely related to privacy-preserving record linkage.  ...  Acknowledgements The authors want to thank Marju Ignatjeva who helped to set up the Sharemind virtual machine for iDASH competition.  ... 
doi:10.1186/s12920-018-0400-8 pmid:30309353 pmcid:PMC6180364 fatcat:66rgv5l2cjh25absw45442tkku

Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications [article]

M. Sadegh Riazi and Christian Weinert and Oleksandr Tkachenko and Ebrahim M. Songhori and Thomas Schneider and Farinaz Koushanfar
2018 arXiv   pre-print
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs.  ...  Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers.  ...  This work has been co-funded by the DFG as part of project E4 within the CRC 1119 CROSSING and by the German Federal Ministry of Education and Research (BMBF) as well as by the Hessen State Ministry for  ... 
arXiv:1801.03239v1 fatcat:6twhd22j25axvf3vq4s73lwl4m

Combining Differential Privacy and Secure Multiparty Computation

Martin Pettai, Peeter Laud
2015 Proceedings of the 31st Annual Computer Security Applications Conference on - ACSAC 2015  
We consider how to perform privacy-preserving analyses on private data from different data providers and containing personal information of many different individuals.  ...  We have implemented a prototype of this combination and the overhead of adding differential privacy to secret sharing is small enough to be usable in practice.  ...  Rmind [3] is a tool for statistical analysis, preserving computational privacy.  ... 
doi:10.1145/2818000.2818027 dblp:conf/acsac/PettaiL15 fatcat:vwuyawwonzgurngwbqe7yeks2a

Maturity and Performance of Programmable Secure Computation

David W. Archer, Dan Bogdanov, Benny Pinkas, Pille Pullonen
2016 IEEE Security and Privacy  
However, continued research in the field as well as increasingly larger real-world deployments suggest that anyone looking for privacy-preserving computing technology keep an eye on the development of  ...  Each of these is illustrated in Table 1 by well-known services that could be replaced with analogous privacy preserving tools.  ... 
doi:10.1109/msp.2016.97 fatcat:6drshm66cvhsznobop2tb7g4ly

Conclave

Nikolaj Volgushev, Malte Schwarzkopf, Ben Getchell, Mayank Varia, Andrei Lapets, Azer Bestavros
2019 Proceedings of the Fourteenth EuroSys Conference 2019 CD-ROM on ZZZ - EuroSys '19  
Our Conclave prototype generates code for cleartext processing in Python and Spark, and for secure MPC using the Sharemind and Obliv-C frameworks.  ...  Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint computations without revealing private data.  ...  Acknowledgements We thank Ran Canetti, Tore Kasper Frederiksen, Derek Leung, and Nickolai Zeldovich for their helpful feedback on drafts of this paper.  ... 
doi:10.1145/3302424.3303982 dblp:conf/eurosys/VolgushevSGVLB19 fatcat:xn7ichau3ndr5kfmvdqkp5wfk4

A Private Lookup Protocol with Low Online Complexity for Secure Multiparty Computation [chapter]

Peeter Laud
2015 Lecture Notes in Computer Science  
We present a secure multiparty computation (SMC) protocol for obliviously reading an element of an array, achieving constant online communication complexity.  ...  Although private lookup is less general than oblivious RAM (ORAM), it allows us to give new and/or more efficient SMC protocols for a number of important computational tasks.  ...  Privacy-preserving graph algorithms have been studied in [5] in a non-composable manner. Composable SSSD protocols for dense graphs have been studied in [1] .  ... 
doi:10.1007/978-3-319-21966-0_11 fatcat:wf2dwtifizearf6ti64ew23xp4

Secure floating point arithmetic and private satellite collision analysis

Liina Kamm, Jan Willemson
2014 International Journal of Information Security  
For this purpose, we first describe basic floating point arithmetic operators (addition and multiplication) for multiparty computations. The operators are implemented on the Sharemind SMC engine.  ...  In this paper, we show that it is possible and, indeed, feasible to use secure multiparty computation (SMC) for calculating the probability of a collision between two satellites.  ...  The authors also thank Nigel Smart for suggesting the use of Chebyshev polynomials, Dan Bogdanov for his help with benchmarking and Sven Laur for coming up with a neat trick for slightly speeding up exponentiation  ... 
doi:10.1007/s10207-014-0271-8 fatcat:7vh4xx562ne6rpgj6vahhv5ibu

Recent Advances in Practical Secure Multi-Party Computation

Satsuya OHATA
2020 IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences  
multi-party computation, privacy-preserving data analysis  ...  Then, we show and discuss current situations on higher-level secure protocols, privacy-preserving data analysis, and frameworks/compilers for implementing MPC applications with low-cost. key words: secure  ...  Recent Advances of Secure Multi-Party Computation In this section, we show recent advances in higher-level secure protocols, privacy-preserving data analysis, and frameworks/compilers for implementing  ... 
doi:10.1587/transfun.2019dmi0001 fatcat:zw747hbb2vfpfl3jdpti4tdeum
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