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

Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

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 combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. 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. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively

Secure, Fast, and Energy-Efficient Outsourced Authentication for Smartphones

Common smartphone authentication mechanisms (e.g., PINs, graphical passwords, and fingerprint scans) are not designed to offer security post-login. Multi-modal continuous authentication addresses this issue by frequently and unobtrusively authenticating the user via behavioral biometric signals, such as touchscreen interaction and hand movements. Because smartphones can easily fall into the hands of the adversary, it is critical that the behavioral biometric information collected and processed on these devices is secured. This can be done by offloading encrypted template information to a remote server, and then performing authentication via privacy-preserving protocols. In this paper, we demonstrate that the energy overhead of current privacy-preserving protocols for continuous authentication is unsustainable on smartphones. To reduce energy consumption, we design a technique that leverages characteristics unique to the authentication setting in order to securely outsource computation to an untrusted Cloud. Our approach is secure against a colluding smartphone and Cloud, thus making it well suited for authentication. We performed extensive experimental evaluation. With our technique, the energy requirement for running an authentication instance that computes Manhattan distance is 0.2 mWh, which corresponds to a negligible fraction of the smartphone\u27s battery capacity. In addition, for Manhattan distance, our protocol runs in 0.72 and 2 s for 8 and 28 biometric features, respectively. We were also able to compute Hamming distance in 3.29 s, compared with 95.57 s achieved with the previous fastest outsourced computation protocol (Whitewash). These results demonstrate that ours is presently the only technique suitable for low-latency continuous authentication (e.g., with authentication scan windows of 60 s or shorter)

OPFE: Outsourcing Computation for Private Function Evaluation

Outsourcing secure multiparty computation(SMC) protocols has allowed resource-constrained devices to take advantage of these developing cryptographic primitives with great efficiency. While the existing constructions for outsourced SMC guarantee input and output privacy, they require that all parties know the function being evaluated. Thus, stronger security guarantees are necessary in applications where the function itself needs to be kept private. We develop the first linear-complexity protocols for outsourcing private function evaluation (PFE), a subset of SMC protocols that provide both input and function privacy. Assuming a semi-honest function holder, we build on the most efficient two-party PFE constructions to develop outsourced protocols that are secure against a semi-honest, covert, or malicious Cloud server and malicious mobile devices providing input to the function. Our protocols require minimal symmetric key operations and only two rounds of communication from the mobile participants. As a secondary contribution, we develop a technique for combining public and private sub-circuits in a single computation called partially-circuit private (PCP) garbling. This novel garbling technique allows us to apply auxiliary circuits to check for malicious behavior using only free-XOR overhead gates rather than the significantly more costly PFE gate construction. These protocols demonstrate the feasibility of outsourced PFE and provide a first step towards developing privacy-preserving applications for use in Cloud computing

Reusable garbled gates for new fully homomorphic encryption service

In this paper, we propose a novel way to provide a fully homomorphic encryption service, namely by using garbled circuits. From a high level perspective, garbled circuits and fully homomorphic encryption, both aim at implementing complex computation on ciphertexts. We define a new cryptographic primitive named reusable garbled gate, which comes from the area of garbled circuits, then based on this new primitive we show that it is very easy to construct a fully homomorphic encryption. However, the instantiation of reusable garbled gates is rather difficult, in fact, we can only instantiate this new primitive based on indistinguishable obfuscation. Furthermore, reusable garbled gates can be a core component for constructing the reusable garbled circuits, which can reduce the communication complexity of them from O(n) to O(1). We believe that reusable garbled gates promise a new way to provide fully homomorphic encryption and reusable garbled circuits service fast.Peer ReviewedPostprint (author's final draft

Private set intersection: A systematic literature review

Secure Multi-party Computation (SMPC) is a family of protocols which allow some parties to compute a function on their private inputs, obtaining the output at the end and nothing more. In this work, we focus on a particular SMPC problem named Private Set Intersection (PSI). The challenge in PSI is how two or more parties can compute the intersection of their private input sets, while the elements that are not in the intersection remain private. This problem has attracted the attention of many researchers because of its wide variety of applications, contributing to the proliferation of many different approaches. Despite that, current PSI protocols still require heavy cryptographic assumptions that may be unrealistic in some scenarios. In this paper, we perform a Systematic Literature Review of PSI solutions, with the objective of analyzing the main scenarios where PSI has been studied and giving the reader a general taxonomy of the problem together with a general understanding of the most common tools used to solve it. We also analyze the performance using different metrics, trying to determine if PSI is mature enough to be used in realistic scenarios, identifying the pros and cons of each protocol and the remaining open problems.This work has been partially supported by the projects: BIGPrivDATA (UMA20-FEDERJA-082) from the FEDER Andalucía 2014– 2020 Program and SecTwin 5.0 funded by the Ministry of Science and Innovation, Spain, and the European Union (Next Generation EU) (TED2021-129830B-I00). The first author has been funded by the Spanish Ministry of Education under the National F.P.U. Program (FPU19/01118). Funding for open access charge: Universidad de Málaga/CBU

PILOT : Practical Privacy-Preserving Indoor Localization Using OuTsourcing

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 500× 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.Peer reviewe

Whitewash: Outsourcing Garbled Circuit Generation for Mobile Devices

Research areas: Information Security and Cryptography, Secure Multiparty Computation, Mobile SecurityGarbled circuits offer a powerful primitive for computation on a user’s personal data while keeping that data private. Despite recent improvements, constructing and evaluating circuits of any useful size remains expensive on the limited hardware resources of a smartphone, the primary computational device available to most users around the world. In this work, we develop a new technique for securely outsourcing the generation of garbled circuits to a Cloud provider. By outsourcing the circuit generation, we are able to eliminate the most costly operations from the mobile device, including oblivious transfers. After proving the security of our techniques in the malicious model, we experimentally demonstrate that our new protocol, built on this role reversal, decreases execution time by 98% and reduces network costs by as much as 63% compared to previous outsourcing protocols. In so doing, we demonstrate that the use of garbled circuits on mobile devices can be made nearly as practical as it is becoming for server-class machines