Garbled Circuits Research Articles

Snail: Secure Single Iteration Localization

Localization is a computer vision task by which the position and orientation of a camera is determined from an image and environmental map. We propose a method for performing localization in a privacy preserving manner supporting two scenarios: first, when the image and map are held by a client who wishes to offload localization to untrusted third parties, and second, when the image and map are held separately by untrusting parties. Privacy preserving localization is necessary when the image and map are confidential, and offloading conserves on-device power and frees resources for other tasks. To accomplish this we integrate existing localization methods and secure multi-party computation (MPC), specifically garbled circuits, yielding proof-based security guarantees in contrast to existing obfuscation-based approaches which recent related work has shown vulnerable. We present two approaches to localization, a baseline data-oblivious adaptation of localization suitable for garbled circuits and our novel Single Iteration Localization. Our technique improves overall performance while maintaining confidentiality of the input image, map, and output pose at the expense of increased communication rounds but reduced computation and communication required per round. Single Iteration Localization is over two orders of magnitude faster than a straightforward application of garbled circuits to localization enabling real-world usage in Turbo the Snail, the first robot to offload localization without revealing input images, environmental map, position, or orientation to offload servers.

More efficient constant-round secure multi-party computation based on optimized Tiny-OT and half-gate

Constand-round secure multi-party computation enables a group of mutually distrustful parties to jointly compute a function of their inputs in a constant-round of communication. In recent years, the constant-round secure multi-party computation protocol has improved the efficiency of the online phase by completing the multiplication computation in advance of the online phase through the Beaver triples generated in the preprocessing phase. However, the preprocessing phase of these protocols is extremely overhead-intensive and there is room for improvement. In this paper, we propose an active, constant-round secure multi-party computation (MPC) protocol which can tolerate arbitrary n − 1 corruptions. At the high level, we follow the Tiny-OT protocol and the unauthenticated garbling for garbled circuits in the preprocessing phase presented by Carmit Hazay and Peter Scholl (ASIACRYPT 2017). Particularly, we first present a new optimized Tiny-OT protocol for generating Beaver (AND) triples which is more efficient than the previous protocol, and then combine the information generated by Tiny-OT to construct an unauthenticated garbled circuit. To be precise, we also reduce the number of commitments and hash function calls in the correctness check of AND triples, and avoid using additional OT operations to compute the circuit, thus further improving communication efficiency. Our new constant-round MPC protocol has a communication complexity O(|C|Bnk) per party. Moreover, we further provide solutions to the open problem of introducing the half-gate technique into multi-party distributed garbled, which will reduce half size of the garbled table per gate.

Privacy-Preserving Truth Discovery for Collaborative-Cloud Encryption in Mobile Crowdsensing

In mobile crowdsensing (MCS) system, a variety of sensors are required to operate together to glean and upload sensory data to clouds for processing. In real practice, truth discovery has been widely explored to find reliable information from various mobile devices. Under the requirement of MCS security, privacy-preserving truth discovery (PPTD) causes wide concern, which refers to discovering truthful information from these unreliable uploaded data while protecting users' private information. Although many PPTD mechanisms have been proposed, they can either not guarantee low communication from users to the cloud, or fail to realize fully strong privacy protect including sensing data privacy, weight privacy, intermediate privacy, and estimated truth privacy. This study designs a collaborative cloud encryption architecture. In this framework, we propose a new system architecture that adapts a two-cloud peer model while leveraging garbled circuit (GC). Users only need to transfer data to two clouds once, which realizes low users workloads while supporting dynamic users. The two cloud terminals cooperate to complete the weight update through the GC but execute the truth discovery algorithm, respectively. In this way, the noninteractive comes true and the users' workloads are transferred to the cloud server side. The weight update finish and weight privacy are fulfilled through GC, meanwhile the other intermediate values maintain strong privacy. At the same time, because the collaborative cloud architecture of the two clouds ensures the confidentiality of the truth value in the cloud and the homomorphism at the inquiry side, it ensures the strong privacy of the whole process from users to inquiries. The performance of this proposed method is verified through extensive evaluations.

PCNNCEC: Efficient and Privacy-Preserving Convolutional Neural Network Inference Based on Cloud-Edge-Client Collaboration

Deploying convolutional neural network (CNN) inference on resource-constrained devices remains a remarkable challenge for industrial Internet of Things (IIoT). Although the cloud computing shows great promise in machine learning training and prediction, outsourcing data to remote cloud always incurs privacy risk and high latency. Therefore, we design a new framework for efficient and privacy-preserving CNN inference based on cloud-edge-client collaboration <formula><tex>$(\rm{named} \, \text{PCNN}_{\text{CEC}})$</tex></formula>. In <formula><tex>$ \text{PCNN}_{\text{CEC}} $</tex></formula>, the model of cloud and the data of client in IIoT are split into two shares and sent to two non-colluded edge servers. By applying the arithmetic secret sharing and pre-computation of beaver's triplets, the two edge servers can jointly calculate the predicting results without learning anything about the model and data. To speed up the pre-computation of offline phase and not sacrifice security, the task of triplets generation is delegated to the cloud, so that the edge servers do not require frequent interactions to generate triplets themselves or introducing additional trusted party. The experimental results show the proposed private comparison protocol achieves a better tradeoff between low latency and high throughput, when it is compared with garbled circuit based protocols and other secret sharing based protocols. Additionally, the benchmarks conducted on realistic MNIST and CIFAR-10 datasets demonstrate that <formula><tex>$ \text{PCNN}_{\text{CEC}} $</tex></formula> costs less communication and runtime than two recently related schemes under the same security level.

Breaking and Fixing Garbled Circuits When a Gate has Duplicate Input Wires

Garbled circuits are a fundamental cryptographic primitive that allows two or more parties to securely evaluate an arbitrary Boolean circuit without revealing any information beyond the output using a constant number of communication rounds. Garbled circuits have been introduced by Yao (FOCS’86) and generalized to the multi-party setting by Beaver, Micali and Rogaway (STOC’90). Since then, several works have improved their efficiency by providing different garbling schemes and several implementations exist. Starting with the seminal Fairplay compiler (USENIX Security’04), several implementation frameworks decoupled the task of compiling the function to be evaluated into a Boolean circuit from the engine that securely evaluates that circuit, e.g., using a secure two-party computation protocol based on garbled circuits. In this paper, we show that this decoupling of circuit generation and evaluation allows a subtle attack on several prominent garbling schemes. It occurs when violating the implicit assumption on the circuit that gates have different input wires which is most often not explicitly specified in the respective papers. The affected garbling schemes use separate calls to a deterministic encryption function for the left and right input wire of a gate to derive pseudo-random encryption pads that are XORed together. When a circuit contains a gate where the left and right input wire are the same, these two per-wire encryption pads cancel out and we demonstrate that this can result in a complete break of privacy. We show how the vulnerable garbling schemes can be fixed easily.

Efficient Privacy-Preserving Face Identification Protocol

Face identification is an important research topic in artificial intelligence and it has wide application in civilian and law enforcement scenarios that require reliable biometric identification. However, the wide deployment of face identification systems has raised serious privacy concerns about the abuse of users' face images. In this paper, we propose an efficient privacy-preserving face identification protocol based on fully homomorphic encryption and garbled circuit. Our work is the first privacy-preserving face identification protocol that guarantees the privacy of the users' face images such that the server cannot learn any information about the face images, access pattern and identification result, while providing face identification service. We formally prove the security of our protocol in a semi-honest model. Experimental results the practicality of our protocol for the large face images database.

Actively Secure Garbled Circuits with Constant Communication Overhead in the Plain Model

Actively Secure Garbled Circuits with Constant Communication Overhead in the Plain Model

Practical reusable garbled circuits with parallel updates

The central contribution of the current paper is to establish a practical and effective reusable garbled circuit scheme with parallel updates, where each update is applied to the circuit in parallel. First of all, we present a strengthened notion of inner product functional encryption (IPE) called simulation-based security (SIM-security) for multi-instance and show that a secret-key IPE scheme is multi-instance SIM-secure. Then we put forward a reusable garbled circuit scheme based on the IPE scheme. The idea is a reduction from re-randomizable garbled circuits in which the distribution of the original garbled circuit is computationally indistinguishable from the re-randomized garbled circuit. Finally, we implemented reusable garbled circuits and validate the practicality of our reusable garbled circuit scheme. Experimental results demonstrate that our reusable garbled circuit scheme is suitable for a wide range of real-world scenarios.

LEGO: A hybrid toolkit for efficient 2PC-based privacy-preserving machine learning

Recently, privacy-preserving machine learning (PPML) has received a lot of research attention, due to the increasing demand for multiple data owners in training machine learning models. Thus, many works have been proposed for privacy-preserving machine learning, in which many breakthroughs have been made. However, However, there is still much room for improvement in terms of efficiency and accuracy. In this paper, we design a toolkit called LEGO that efficiently combines garbled circuits, secret sharing, oblivious transfer, and homomorphic encryption to reduce time and improve accuracy. Our work focuses on the two-server model where the two servers train the models on the different owners’ private data. For fully connected layers and convolutional layers, we utilize the associated multiplication triplets to reduce the communication. For the offline phase, we design a novel protocol based on oblivious transfer and improved the previous protocol based on homomorphic encryption. We use the MPC-friendly activation function to improve the performance. Besides, we accelerate the local computation by adding GPU support. Our work is implemented in C++, and it can securely train machine learning models, including linear regression, logistic regression, neural networks, and convolutional neural networks. The experimental results show that our work is significantly faster than the state-of-the-art works.

Privacy-preserving parallel kNN classification algorithm using index-based filtering in cloud computing.

With the development of cloud computing, interest in database outsourcing has recently increased. In cloud computing, it is necessary to protect the sensitive information of data owners and authorized users. For this, data mining techniques over encrypted data have been studied to protect the original database, user queries and data access patterns. The typical data mining technique is kNN classification which is widely used for data analysis and artificial intelligence. However, existing works do not provide a sufficient level of efficiency for a large amount of encrypted data. To solve this problem, in this paper, we propose a privacy-preserving parallel kNN classification algorithm. To reduce the computation cost for encryption, we propose an improved secure protocol by using an encrypted random value pool. To reduce the query processing time, we not only design a parallel algorithm, but also adopt a garbled circuit. In addition, the security analysis of the proposed algorithm is performed to prove its data protection, query protection, and access pattern protection. Through our performance evaluation, the proposed algorithm shows about 2∼25 times better performance compared with existing algorithms.

PShare: Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy.

Secure Similar Sequence Query over Multi-source Genomic Data on Cloud

Cloud computing has been shown promising in enabling various analyses over large-scale genomic data integrated across multiple data sources. However, outsourcing data to remote cloud servers raises data-privacy concerns, therefore demands secure computing measures over the data analyzing process on the untrusted cloud servers. Due to the scale of genomic dataset and the length of each genomic sequence, it is challenging to evaluate data-analysis functions on outsourced genomic data securely and efficiently. In this work, we study the secure similar-sequence-query (SSQ) problem over outsourced genomic data. To address the challenges of security and efficiency, we propose a set of two-party computing protocols in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixed form</i> , which combine secure secret sharing, garbled circuit, and partial homomorphic encryptions together and use them to jointly fulfill the secure SSQ function. Moreover, our scheme supports the fusion of genomic data from multiple data owners to generate a deduplicated-joint genomic dataset, therefore reduces the redundancy in the dataset. The performance improvements of our scheme are validated through extensive experiments on a commercial cloud platform over a real-world genomic dataset.

Efficient privacy-preserving face verification scheme

Face verification has been a promising artificial intelligence technique and is becoming increasingly popular in civilian and law enforcement applications that require reliable biometric authentication. However, the wide deployment of face verification systems has raised serious privacy concerns regarding the abuse of users’ face images. In this paper, we present an efficient privacy-preserving face verification scheme based on fully homomorphic encryption and garbled circuit. Our protocol is the first one that guarantees that the server cannot learn anything about users’ face images and the verification result while providing the face verification service, and the client learns nothing beyond the verification result. Experimental results show that our scheme takes less than one second to perform a privacy-preserving face verification.

A novel privacy-preserving deep learning scheme without a cryptography component

Recently, deep learning using Convolutional Neural Networks has played an essential role in many fields. Traditional cryptography, such as the technologies of the garbled circuit and the homomorphic encryption, may provide both parties with a private and secure computation in the neural networks as well as a secure inference scheme. However, it suffers heavy computation in practical designs especially for the training of a CNN model. Hence, the scalability of the model is restricted by these components. In this paper, we propose a novel deep learning model and a secure inferencing scheme in an application of a neural network. We utilize the inherent properties of a convolutional neural network to design a secure mechanism without using any complicated cryptography component. The security analysis shows our proposed scheme is secure, and the experimental results demonstrate that our method is very efficient and suitable for practical applications.

Gage MPC: Bypassing Residual Function Leakage for Non-Interactive MPC

Abstract Existing models for non-interactive MPC cannot provide full privacy for inputs, because they inherently leak the residual function (i.e., the output of the function on the honest parties’ input together with all possible values of the adversarial inputs). For example, in any non-interactive sealed-bid auction, the last bidder can figure out what was the highest previous bid. We present a new MPC model which avoids this privacy leak. To achieve this, we utilize a blockchain in a novel way, incorporating smart contracts and arbitrary parties that can be incentivized to perform computation (“bounty hunters,” akin to miners). Security is maintained under a monetary assumption about the parties: an honest party can temporarily supply a recoverable collateral of value higher than the computational cost an adversary can expend. We thus construct non-interactive MPC protocols with strong security guarantees (full security, no residual leakage) in the short term. Over time, as the adversary can invest more and more computational resources, the security guarantee decays. Thus, our model, which we call Gage MPC, is suitable for secure computation with limited-time secrecy, such as auctions. A key ingredient in our protocols is a primitive we call “Gage Time Capsules” (GaTC): a time capsule that allows a party to commit to a value that others are able to reveal but only at a designated computational cost. A GaTC allows a party to commit to a value together with a monetary collateral. If the original party properly opens the GaTC, it can recover the collateral. Otherwise, the collateral is used to incentivize bounty hunters to open the GaTC. This primitive is used to ensure completion of Gage MPC protocols on the desired inputs. As a requisite tool (of independent interest), we present a generalization of garbled circuit that are more robust: they can tolerate exposure of extra input labels. This is in contrast to Yao’s garbled circuits, whose secrecy breaks down if even a single extra label is exposed. Finally, we present a proof-of-concept implementation of a special case of our construction, yielding an auction functionality over an Ethereum-like blockchain.

Functional encryption for cubic polynomials and implementation

Functional encryption (FE), which provides fine-grained access control on encrypted data, is becoming a new hot spot in the field of cryptography. Recent applications, such as outsourcing computation, searchable encryption and so on, suggest that FE has unlimited possibilities. It especially shows great feasibility to construct indistinguishability obfuscation and reuseable garbled circuits. Furthermore, bounded collusion functional encryption is an extension of FE which is against more than one key query and protects the security of messages under more than one function keys. In this paper, we proposed a bounded collusion FE for cubic polynomials, which follows from Agrawal and Rosen's work on TCC 2017. Our construction only invokes the Regev public key encryption and a linear FE scheme which avoids complex encodings defined recursively. What's more, we proposes an FE scheme for all circuit with FULL-SIM security. Finally, we also implement these schemes and do some analyses on parameters' size, time and space performance.

Identity-based Encryption from the Diffie-Hellman Assumption

We provide the first constructions of identity-based encryption and hierarchical identity-based encryption based on the hardness of the (Computational) Diffie-Hellman Problem (without use of groups with pairings) or Factoring. Our construction achieves the standard notion of identity-based encryption as considered by Boneh and Franklin [CRYPTO 2001]. We bypass known impossibility results using garbled circuits that make a non-black-box use of the underlying cryptographic primitives.

An Efficient HPRA-Based Multiclient Verifiable Computation: Transform and Instantiation

Choi, Katz, Kumaresan, and Cid put forward the conception of multiclient noninteractive verifiable computation (MVC), enabling a group of clients to outsource computation of a function of f . CKKC’s MVC is impractical due to their dependence on fully homomorphic encryption (FHE) and garbled circuits (GCs). In this paper, with the goal of satisfying practical requirements, a general transform is presented from the homomorphic proxy re-authenticator (HPRA) of Deler, Ramacher, and Slamanig to MVC schemes. MVC constructions in this particular study tend to be more efficient once the underlying HPRA avoids introducing FHE and GCs. By deploying the transform to DRS’s HPRA scheme, a specific MVC scheme for calculating the linear combinations of vectors has been proposed. It can be understood that it is the first feasible and implementable MVC scheme so far, and the instantiation solution has a great advantage in efficiency compared with related works.

Private Trajectory Intersection Testing: Is Garbled Circuit Better than Custom Protocols?

In this paper, two protocols are presented for private intersection detection of two moving objects’ trajectories. Assuming that the movement trajectory of an object can be described by a time-dependent polynomial function, the problem of finding the intersection points is simplified to the problem of finding the common roots of the corresponding polynomials. Thereafter, GrObner Basis is used to design a novel secure protocol of finding the common roots of the polynomials. Another protocol is also designed based on the distance computation of two trajectories’ curves. Moreover, we present the complexity analysis of the protocol for private trajectory intersection testing of two moving objects, which is based on GrObner Basis. Then, we compare its complexity by the garbled circuit-based protocol for Euclidean Distance Computation of l points. We also prove the security of our proposed protocol, which is based on the distance computation of two curves. Keywords: Private Trajectory Intersection Testing, GrObner Basis, Distance Computation, Complexity , Garbled Circuit, Euclidean Distance

Secure Privacy-Preserving Association Rule Mining With Single Cloud Server

To preserve the privacy of data uploaded on the cloud, it is widely accepted to encrypt the data before uploading it. This leads to the challenge of data analysis, especially association rule mining while protecting data privacy. As one of the solutions, homomorphic encryption is presented allowing encrypted data processing without decryption. In particular, the twin-cloud structure is frequently applied in the privacy-preserving association rule mining schemes based on asymmetric homomorphic encryption, which contradicts the reality that most of the practical applications applied the single cloud server. However, the existing related single cloud server schemes suffer from privacy leakage problems. To fill this gap in the literature, in this paper, we first present a universal secure multiplication protocol with the single cloud server using the garbled circuit and additive homomorphic encryption. Based on this multiplication protocol, we construct the inner product protocol, comparison protocol, frequent itemset protocol, and the final association rule mining protocol that is secure against privacy leakage. Finally, we give the theoretical security analysis of the proposed protocols and show its performance analysis.