From Keys to Databases—Real-World Applications of Secure Multi-Party Computation

Archer, David W.; Bogdanov, Dan; Lindell, Yehuda; Kamm, Liina; Nielsen, Kurt; Pagter, Jakob; Smart, Nigel P.; Wright, Rebecca N.

doi:10.1093/comjnl/bxy090

Cited by 85 publications

(77 citation statements)

References 29 publications

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“…It is important to note that by using the outsourcing setting we achieve practical PPIL with security against malicious PPIL clients and malicious PPIL servers, whereas in the client/server setting even solutions with substantially weaker security against a semihonest client currently have impractical runtimes and communication. Furthermore, the outsourcing model with multiple STTPs is widely adopted not only in recent academic papers, e.g., for private machine learning [70,71], and genomic privacy [72,73,74,75,76,77], to name just a few, but also deployed in industrial products, see [78] for a few examples.…”

Section: A Outsourcing Schemementioning

confidence: 99%

PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

Järvinen

Leppäkoski

Lohan

et al. 2019

2019 IEEE European Symposium on Security and Privacy (EuroS&P)

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In the last decade, we observed a constantly growing number of Location-Based Services (LBSs) used in indoor environments, such as for targeted advertising in shopping malls or finding nearby friends. Although privacy-preserving LBSs were addressed in the literature, there was a lack of attention to the problem of enhancing privacy of indoor localization, i.e., the process of obtaining the users' locations indoors and, thus, a prerequisite for any indoor LBS.In this work we present PILOT, the first practically efficient solution for Privacy-Preserving Indoor Localization (PPIL) that was obtained by a synergy of the research areas indoor localization and applied cryptography. We design, implement, and evaluate protocols for Wi-Fi fingerprint-based PPIL that rely on 4 different distance metrics. To save energy and network bandwidth for the mobile end devices in PPIL, we securely outsource the computations to two non-colluding semi-honest parties. Our solution mixes different secure two-party computation protocols and we design size-and depth-optimized circuits for PPIL. We construct efficient circuit building blocks that are of independent interest: Single Instruction Multiple Data (SIMD) capable oblivious access to an array with low circuit depth and selection of the k-Nearest Neighbors with small circuit size. Additionally, we reduce Received Signal Strength (RSS) values from 8 bits to 4 bits without any significant accuracy reduction. Our most efficient PPIL protocol is 553x faster than that of Li et al. (INFOCOM'14) and 500x faster than that of Ziegeldorf et al. (WiSec'14). Our implementation on commodity hardware has practical run-times of less than 1 second even for the most accurate distance metrics that we consider, and it can process more than half a million PPIL queries per day. 448 2019 IEEE European Symposium on Security and Privacy (EuroS&P)

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Section: A Outsourcing Schemementioning

confidence: 99%

PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

Järvinen

Leppäkoski

Lohan

et al. 2019

2019 IEEE European Symposium on Security and Privacy (EuroS&P)

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“…Archer et al [121] survey some of the more current uses of SMC in the real world, including the Private Data as a Service (PDaaS) system of Jana (https://galois.com/project/janaprivate-data-as-a-service/), ongoing commercial efforts by Partisia (https://partisia.com/mpc-goes-live/) (which powered the Danish sugar-beet auction mentioned before), and protection of corporate secrets by Unbound Tech (https://www.unboundtech.com/). SMC has also garnered the interest of legislative bodies.…”

Section: Real-world Deploymentmentioning

confidence: 99%

Secure Multiparty Computation and Trusted Hardware: Examining Adoption Challenges and Opportunities

Choi

Butler

2019

Security and Communication Networks

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When two or more parties need to compute a common result while safeguarding their sensitive inputs, they use secure multiparty computation (SMC) techniques such as garbled circuits. The traditional enabler of SMC is cryptography, but the significant number of cryptographic operations required results in these techniques being impractical for most real-time, online computations. Trusted execution environments (TEEs) provide hardware-enforced isolation of code and data in use, making them promising candidates for making SMC more tractable. This paper revisits the history of improvements to SMC over the years and considers the possibility of coupling trusted hardware with SMC. This paper also addresses three open challenges: (1) defeating malicious adversaries, (2) mobile-friendly TEE-supported SMC, and (3) a more general coupling of trusted hardware and privacy-preserving computation.

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“…Companies including Ciphercloud [13], Navajo Systems [26], Skyhigh Networks [61], and Perspecsys [68] deploy proprietary PRE schemes to enable searching and sorting of ciphertexts stored within software-as-aservice (SaaS) systems. SAP's SEEED system [75], IQcrypt [47], Microsoft's Always Encrypted [58], and Galois's Jana [5] explicitly use privacy schema where some columns are strongly encrypted and other columns are DEor ORE-encrypted.…”

Section: Related Workmentioning

confidence: 99%

The tao of inference in privacy-protected databases

et al. 2018

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To protect database confidentiality even in the face of full compromise while supporting standard functionality, recent academic proposals and commercial products rely on a mix of encryption schemes. The recommendation is to apply strong, semantically secure encryption to the "sensitive" columns and protect other columns with property-revealing encryption (PRE) that supports operations such as sorting. We design, implement, and evaluate a new methodology for inferring data stored in such encrypted databases. The cornerstone is the multinomial attack, a new inference technique that is analytically optimal and empirically outperforms prior heuristic attacks against PRE-encrypted data. We also extend the multinomial attack to take advantage of correlations across multiple columns. This recovers PREencrypted data with sufficient accuracy to then apply machine learning and record linkage methods to infer columns protected by semantically secure encryption or redaction. We evaluate our methodology on medical, census, and union-membership datasets, showing for the first time how to infer full database records. For PRE-encrypted attributes such as demographics and ZIP codes, our attack outperforms the best prior heuristic by a factor of 16. Unlike any prior technique, we also infer attributes, such as incomes and medical diagnoses, protected by strong encryption. For example, when we infer that a patient in a hospital-discharge dataset has a mental health or substance abuse condition, this prediction is 97% accurate.

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From Keys to Databases—Real-World Applications of Secure Multi-Party Computation

Cited by 85 publications

References 29 publications

PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

Secure Multiparty Computation and Trusted Hardware: Examining Adoption Challenges and Opportunities

The tao of inference in privacy-protected databases

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