For your phone only: custom protocols for efficient secure function evaluation on mobile devices

Carter, Henry; Amrutkar, Chaitrali; Dacosta, Italo; Traynor, Patrick

doi:10.1002/sec.851

Cited by 24 publications

(18 citation statements)

References 26 publications

(48 reference statements)

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“…Implementations of PSI protocols on smartphones such as [4,10,33] can be found in the literature, but they either do not achieve linear complexity or do not consider the offline/online setting, and hence are not suited for our scenario.…”

Section: Related Work On Psimentioning

confidence: 99%

Private Set Intersection for Unequal Set Sizes with Mobile Applications

Kiss

Liu

Schneider

et al. 2017

Proceedings on Privacy Enhancing Technologies

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Private set intersection (PSI) is a cryptographic technique that is applicable to many privacysensitive scenarios. For decades, researchers have been focusing on improving its efficiency in both communication and computation. However, most of the existing solutions are inefficient for an unequal number of inputs, which is common in conventional client-server settings.In this paper, we analyze and optimize the efficiency of existing PSI protocols to support precomputation so that they can efficiently deal with such input sets. We transform four existing PSI protocols into the precomputation form such that in the setup phase the communication is linear only in the size of the larger input set, while in the online phase the communication is linear in the size of the smaller input set. We implement all four protocols and run experiments between two PCs and between a PC and a smartphone and give a systematic comparison of their performance. Our experiments show that a protocol based on securely evaluating a garbled AES circuit achieves the fastest setup time by several orders of magnitudes, and the fastest online time in the PC setting where AES-NI acceleration is available. In the mobile setting, the fastest online time is achieved by a protocol based on the Diffie-Hellman assumption.

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Section: Related Work On Psimentioning

confidence: 99%

Private Set Intersection for Unequal Set Sizes with Mobile Applications

Kiss

Liu

Schneider

et al. 2017

Proceedings on Privacy Enhancing Technologies

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“…Carter et al recently proposed this for mobile devices [7,8,9]. We build on this model, but further allow circuit generation to be also outsourced, such that the second party can be also a computationally weak device.…”

Section: Outsourcingmentioning

confidence: 99%

“…This is envisioned in [7,8], but only achieved for one computationally weak device -the one that does not generate the garbled circuit. They do this using any garbling scheme as defined by Bellare et al by sharing the encoding and decoding keys e, d. The cloud would then perform the evaluation function Ev.…”

Section: Multi-user Scenariomentioning

confidence: 99%

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Oblivious outsourcing of garbled circuit generation

Kerschbaum

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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Yao's garbled circuit technique is often used in outsourced computation. Current approaches divide the computation to two or more servers. The assumption is that the servers collaborate in offering the outsourced computation, but do not share data. This seems somewhat paradoxical in the current cloud economy. We therefore propose oblivious outsourcing where one server is unaware that other servers are involved. We present a garbled circuit generation outsourcing scheme built on lattice-based cryptography implementing this model. Our scheme does not increase the cost of circuit evaluation, but achieves a speed up of 98% (factor 55) for circuit generation.

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“…Nikolaenko et al [51,50] use similar non-collusion assumption where they trust the third party (garbled circuit generator) not to collude with the garbled circuit evaluator. Protocols for outsourcing multi-party computation [23,22,42,43] use similar non-collusion assumptions to outsource evaluation of the garbled circuits.…”

Section: Preliminariesmentioning

confidence: 99%

Controlled Functional Encryption

Naveed

Agrawal

Prabhakaran

et al. 2014

Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security

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Motivated by privacy and usability requirements in various scenarios where existing cryptographic tools (like secure multi-party computation and functional encryption) are not adequate, we introduce a new cryptographic tool called Controlled Functional Encryption (C-FE). As in functional encryption, C-FE allows a user (client) to learn only certain functions of encrypted data, using keys obtained from an authority. However, we allow (and require) the client to send a fresh key request to the authority every time it wants to evaluate a function on a ciphertext. We obtain efficient solutions by carefully combining CCA2 secure public-key encryption (or rerandomizable RCCA secure public-key encryption, depending on the nature of security desired) with Yao's garbled circuit. Our main contributions in this work include developing and formally defining the notion of C-FE; designing theoretical and practical constructions of C-FE schemes achieving these definitions for specific and general classes of functions; and evaluating the performance of our constructions on various application scenarios.

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For your phone only: custom protocols for efficient secure function evaluation on mobile devices

Cited by 24 publications

References 26 publications

Private Set Intersection for Unequal Set Sizes with Mobile Applications

Private Set Intersection for Unequal Set Sizes with Mobile Applications

Oblivious outsourcing of garbled circuit generation

Controlled Functional Encryption

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