Reuse It Or Lose It: More Efficient Secure Computation Through Reuse of Encrypted Values

Two-party secure function evaluation (SFE) has become significantly more feasible, even on resource-constrained devices, because of advances in server-aided computation systems. However, there are still bottlenecks, particularly in the input validation stage of a computation. Moreover, SFE research has not yet devoted sufficient attention to the important problem of retaining state after a computation has been performed so that expensive processing does not have to be repeated if a similar computation is done again. This paper presents PartialGC, an SFE system that allows the reuse of encrypted values generated during a garbled-circuit computation. We show that using PartialGC can reduce computation time by as much as 96% and bandwidth by as much as 98% in comparison with previous outsourcing schemes for secure computation. We demonstrate the feasibility of our approach with two sets of experiments, one in which the garbled circuit is evaluated on a mobile device and one in which it is evaluated on a server. We also use PartialGC to build a privacy-preserving "friend finder" application for Android. The reuse of previous inputs to allow stateful evaluation represents a new way of looking at SFE and further reduces computational barriers.Comment: 20 pages, shorter conference version published in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Pages 582-596, ACM New York, NY, US

Secure Outsourced Biometric Authentication with Performance Evaluation on Smartphones

Abstract-We design privacy-preserving protocols for Scaled Manhattan and Scaled Euclidean verifiers, secure against malicious clients and honest-but-curious server. We then augment our protocols with principal component analysis (PCA), which can help improve authentication accuracy. We evaluate the performance of our protocols on an emerging application-namely, continuous authentication of smartphone users. We compare the performance of protocols secure under the malicious client model, with three protocols secure in the honest-but-curious model. We report tradeoffs between computation overhead, communication cost, and authentication accuracy. Our key observations are: 1) Scaled Manhattan without PCA gives the best tradeoff between security, accuracy, and overhead; and 2) with PCA, memory availability on current smartphones limits the number of features that can be used with Scaled Manhattan, and prevents the Scaled Euclidean protocol from running. Our extended evaluation on a laptop client shows that PCA with both Scaled Manhattan and Scaled Euclidean verifiers is feasible given sufficient memory

Secure Two-Party Computation is Practical

Secure multi-party computation has been considered by the cryptographic community for a number of years. Until recently it has been a purely theoretical area, with few implementations with which to test various ideas. This has led to a number of optimisations being proposed which are quite restricted in their application. In this paper we describe an implementation of the two-party case, using Yao’s garbled circuits, and present various algorithmic protocol improvements. These optimisations are analysed both theoretically and empirically, using experiments of various adversarial situations. Our experimental data is provided for reasonably large circuits, including one which performs an AES encryption, a problem which we discuss in the context of various possible applications

How to use bitcoin to incentivize correct computations.

ABSTRACT We study a model of incentivizing correct computations in a variety of cryptographic tasks. For each of these tasks we propose a formal model and design protocols satisfying our model's constraints in a hybrid model where parties have access to special ideal functionalities that enable monetary transactions. We summarize our results: • Verifiable computation. We consider a setting where a delegator outsources computation to a worker who expects to get paid in return for delivering correct outputs. We design protocols that compile both public and private verification schemes to support incentivizations described above. • Secure computation with restricted leakage. Building on the recent work of Huang et al. (Security and Privacy 2012), we show an efficient secure computation protocol that monetarily penalizes an adversary that attempts to learn one bit of information but gets detected in the process. • Fair secure computation. Inspired by recent work, we consider a model of secure computation where a party that aborts after learning the output is monetarily penalized. We then propose an ideal transaction functionality F ML and show a constant-round realization on the Bitcoin network. Then, in the F ML -hybrid world we design a constant round protocol for secure computation in this model. • Noninteractive bounties. We provide formal definitions and candidate realizations of noninteractive bounty mechanisms on the Bitcoin network which (1) allow a bounty maker to place a bounty for the solution of a hard problem by sending a single message, and (2) allow a bounty collector (unknown at the time of bounty creation) with the solution to claim the bounty, while (3) ensuring that the bounty maker can learn the solution whenever its bounty is collected, and (4) preventing malicious eavesdropping parties from both claiming the bounty as well as learning the solution. All our protocol realizations (except those realizing fair secure computation) rely on a special ideal functionality that is not curPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. rently supported in Bitcoin due to limitations imposed on Bitcoin scripts. Motivated by this, we propose validation complexity of a protocol, a formal complexity measure that captures the amount of computational effort required to validate Bitcoin transactions required to implement it in Bitcoin. Our protocols are also designed to take advantage of optimistic scenarios where participating parties behave honestly

Efficient Two Party and Multi Party Computation Against Covert Adversaries

Abstract. Recently, Aumann and Lindell introduced a new realistic security model for secure computation, namely, security against covert adversaries. The main motivation was to obtain secure computation protocols which are efficient enough to be usable in practice. Aumann and Lindell presented an efficient two party computation protocol secure against covert adversaries. They were able to utilize cut and choose techniques rather than relying on expensive zero knowledge proofs. In this paper, we design an efficient multi-party computation protocol in the covert adversary model which remains secure even if a majority of the parties are dishonest. We also substantially improve the two-party protocol of Aumann and Lindell. Our protocols avoid general NP-reductions and only make a black box use of efficiently implementable cryptographic primitives. Our two-party protocol is constant-round while the multiparty one requires a logarithmic (in number of parties) number of rounds of interaction between the parties. Our protocols are secure as per the standard simulation-based definitions of security. Although our main focus is on designing efficient protocols in the covert adversary model, the techniques used in our two party case directly generalize to improve the efficiency of two party computation protocols secure against standard malicious adversaries.

Protocols for Secure Computation on Privately Encrypted Data in the Cloud

Cloud services provide clients with highly scalable network, storage, and computational resources. However, these service come with the challenge of guaranteeing the confidentiality of the data stored on the cloud. Rather than attempting to prevent adversaries from compromising the cloud server, we aim in this thesis to provide data confidentiality and secure computations in the cloud, while preserving the privacy of the participants and assuming the existence of a passive adversary able to access all data stored in the cloud. To achieve this, we propose several protocols for secure and privacy-preserving data storage in the cloud. We further show their applicability and scalability through their implementations. we first propose a protocol that would allow emergency providers access to privately encrypted data in the cloud, in the case of an emergency, such as medical records. Second, we propose various protocols to allow a querying entity to securely query privately encrypted data in the cloud while preserving the privacy of the data owners and the querying entity. We also present cryptographic and non-cryptographic protocols for secure private function evaluation in order to extend the functions applicable in the protocols