Secure Two-party Computation Research Articles

Complete Characterization of Fairness in Secure Two-Party Computation of Boolean Functions

Complete Characterization of Fairness in Secure Two-Party Computation of Boolean Functions

Privadome: Delivery Drones and Citizen Privacy

E-commerce companies are actively considering the use of delivery drones for customer fulfillment, leading to growing concerns around citizen privacy. Drones are equipped with cameras, and the video feed from these cameras is often required as part of routine navigation, be it for semi-autonomous or fully-autonomous drones. Footage of ground-based citizens captured in these videos may lead to privacy concerns. This paper presents Privadome, a system that implements the vision of a virtual privacy dome centered around the citizen. Privadome is designed to be integrated with city-scale regulatory authorities that oversee delivery drone operations and realizes this vision through two components, PD-MPC and PD-ROS. PD-MPC allows citizens equipped with a mobile device to identify drones that have captured their footage. It uses secure two-party computation to achieve this goal without compromising the privacy of the citizen’s location. PD-ROS allows the citizen to communicate with such drones and obtain an audit trail showing how the drone uses their footage and determine if privacy-preserving steps are taken to sanitize the footage.

Correlated Randomness Reduction in Domain-Restricted Secure Two-Party Computation

Correlated Randomness Reduction in Domain-Restricted Secure Two-Party Computation

Low Communication-Cost PSI Protocol for Unbalanced Two-Party Private Sets

Two-party private set intersection (PSI) plays a pivotal role in secure two-party computation protocols. The communication cost in a PSI protocol is normally influenced by the sizes of the participating parties. However, for parties with unbalanced sets, the communication costs of existing protocols mainly depend on the size of the larger set, leading to high communication cost. In this paper, we propose a low communication-cost PSI protocol designed specifically for unbalanced two-party private sets, aiming to enhance the efficiency of communication. For each item in the smaller set, the receiver queries whether it belongs to the larger set, such that the communication cost depends solely on the smaller set. The queries are implemented by private information retrieval which is constructed with trapdoor hash function. Our investigation indicates that in each instance of invoking the trapdoor hash function, the receiver is required to transmit both a hash key and an encoding key to the sender, thus incurring significant communication cost. In order to address this concern, we propose the utilization of a seed hash key, a seed encoding key, and a Latin square. By employing these components, the sender can autonomously generate all the necessary hash keys and encoding keys, obviating the multiple transmissions of such keys. The proposed protocol is provably secure against a semihonest adversary under the Decisional Diffie–Hellman assumption. Through implementation demonstration, we showcase that when the sizes of the two sets are 28 and 214, the communication cost of our protocol is only 3.3% of the state-of-the-art protocol and under 100 Kbps bandwidth, we achieve 1.46x speedup compared to the state-of-the-art protocol. Our source code is available on GitHub: https://github.com/TAN-OpenLab/Unbanlanced-PSI.

Retracted: Secure Two-Party Computation Based on Fast Cut-and-Choose Bilateral Oblivious Transfer

Retracted: Secure Two-Party Computation Based on Fast Cut-and-Choose Bilateral Oblivious Transfer

EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

Sok: vector OLE-based zero-knowledge protocols

A zero-knowledge proof is a cryptographic protocol where a prover can convince a verifier that a statement is true, without revealing any further information except for the truth of the statement. This article is a survey of recent developments in building practical zero-knowledge proof systems using vector oblivious linear evaluation (VOLE), a tool from secure two-party computation. In this work, we attempt to systematize the recent works on VOLE-based Zero-Knowledge proofs and make the state of the art accessible in one document.

PPOLQ: Privacy-Preserving Optimal Location Query With Multiple-Condition Filter in Outsourced Environments

The optimal location selection is one type of the location-based services (LBS) that aims to find the best location for a new facility from some candidate facilities given a set of existing facilities and a set of customers. Due to reliable and flexible cloud services, outsourcing such heavy-computation tasks has been a popular trend. However, since the cloud is not fully trusted, and the location data contains the sensitive information, privacy protection becomes an essential requirement for these services. Although some related works have been proposed to provide privacy protection, the privacy of data and queries, accuracy of query results, and multiple features of location data are not considered by them simultaneously. In this paper, we propose a privacy-preserving optimal location query scheme PPOLQ that supports multiple-condition filter and queries over multiple data providers in outsourced environments. Specifically, we first design a secure division protocol and a secure inner product protocol based on the Paillier algorithm and the random masking technique, respectively. After that, based on the proposed algorithms, the additive homomorphic encryption, and the secure two-party computation techniques, we develop a privacy-preserving optimal location query scheme. Finally, we analyze the security of our proposed algorithms and scheme in the semi-honest model. Meanwhile, we implement all algorithms and the proposed scheme, and our implementation is open source at Gitee. We also evaluate their performances using synthetic datasets, and extensive experiments show that our scheme is practical for the real-world applications.

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.

Garbled Circuits from an SCA Perspective

Garbling schemes, invented in the 80’s by Yao (FOCS’86), have been a versatile and fundamental tool in modern cryptography. A prominent application of garbled circuits is constant round secure two-party computation, which led to a long line of study of this object, where one of the most influential optimizations is Free-XOR (Kolesnikov and Schneider ICALP’08), introducing a global offset Δ for all garbled wire values where XOR gates are computed locally without garbling them. To date, garbling schemes were not studied per their side-channel attacks (SCA) security characteristics, even though SCA pose a significant security threat to cryptographic devices. In this research we, demonstrate that adversaries utilizing advanced SCA tools such as horizontal attacks, mixed with advanced hypothesis building and standard (vertical) SCA tools, can jeopardize garbling implementations.Our main observation is that garbling schemes utilizing a global secret Δ open a door to quite trivial side-channel attacks. We model our side-channel attacks on the garbler’s device and discuss the asymmetric setting where various computations are not performed on the evaluator side. This enables dangerous leakage extraction on the garbler and renders our attack impossible on the evaluator’s side.Theoretically, we first demonstrate on a simulated environment, that such attacks are quite devastating. Concretely, our attack is capable of extracting Δ when the circuit embeds only 8 input non-linear gates with fifth/first-order attack Success-Rates of 0.65/0.7. With as little as 3 such gates, our attack reduces the first-order Guessing Entropy of Δ from 128 to ∼ 48-bits. We further demonstrate our attack via an implementation and power measurements data over an STM 32-bit processor software implementing circuit garbling, and discuss their limitations and mitigation tactics on logical, protocol and implementation layers.

Clustered Federated Multitask Learning on Non-IID Data With Enhanced Privacy

Federated learning is a machine learning prgadigm that enables the collaborative learning among clients while keeping the privacy of clients’ data. Federated multitask learning (FMTL) deals with the statistic challenge of non-independent and identically distributed (IID) data by training a personalized model for each client, and yet requires all the clients to be always online in each training round. To eliminate the limitation of full-participation, we explore multitask learning associated with model clustering, and first propose a clustered FMTL to achieve the multual-task learning on non-IID data, while simultaneously improving the communication efficiency and the model accuracy. To enhance its privacy, we adopt a general dual-server architecture and further propose a secure clustered FMTL by designing a series of secure two-party computation protocols. The convergence analysis and security analysis is conducted to prove the correctness and security of our methods. Numeric evaluation on public data sets validates that our methods are superior to state-of-the-art methods in dealing with non-IID data while protecting the privacy.

Privacy-preserving Naive Bayes classification based on secure two-party computation

<abstract><p>With the proliferation of data and machine learning techniques, there is a growing need to develop methods that enable collaborative training and prediction of sensitive data while preserving privacy. This paper proposes a new protocol for privacy-preserving Naive Bayes classification using secure two-party computation (STPC). The key idea is to split the training data between two non-colluding servers using STPC to train the model without leaking information. The servers secretly share their data and the intermediate computations using cryptographic techniques like Beaver's multiplication triples and Yao's garbled circuits. We implement and evaluate our protocols on the MNIST dataset, demonstrating that they achieve the same accuracy as plaintext computation with reasonable overhead. A formal security analysis in the semi-honest model shows that the scheme protects the privacy of the training data. Our work advances privacy-preserving machine learning by enabling secure outsourced Naive Bayes classification with applications such as fraud detection, medical diagnosis, and predictive analytics on confidential data from multiple entities. The modular design allows embedding different secure matrix multiplication techniques, making the framework adaptable. This line of research paves the way for practical and secure data mining in a distributed manner, upholding stringent privacy regulations.</p></abstract>

Efficient Privacy-Preserving Inference Outsourcing for Convolutional Neural Networks

Inference outsourcing enables model owners to deploy their machine learning models on cloud servers to serve users. In this paradigm, the privacy of model owners and users should be considered. Existing solutions focus on Convolutional Neural Networks (CNNs) but their efficiency is much lower than GALA, which is a solution that only protects user privacy. Furthermore, these solutions adopt approximations that reduce the model accuracy and thus require model owners to retrain the models. In this paper, we present an efficient CNN inference outsourcing solution that protects the privacy of both model owners and users. Specifically, we design secure two-party computation protocols based on two non-colluding cloud servers, which calculate with additive secret shares of the model and the user’s input. Our protocols avoid the expensive permutation operations in linear calculations and approximations in non-linear calculations. We implement our solution on realistic CNNs and experimental results show that our solution is even 2–4 times faster than GALA.

Privacy‐preserving CNN feature extraction and retrieval over medical images

Online medicine diagnosis based on pathological images has been regarded as a pervasive method due to the advances in electronic healthcare and Internet of Things (IoT), however, it also causes storage and computing stress on the local IoT devices. To solve this problem, a nature way is to outsource images to cloud servers. Unfortunately, a range of security and privacy issues arise while delegating both storage and computing to the untrusted external servers. In this paper, we present a privacy-preserving feature extraction and retrieval scheme over medical images, which allows images storage and processing on two separate cloud servers. We share the images using secret sharing technology and design a set of secure two-party computation protocols between the two cloud servers. Then a privacy-preserving convolutional neural networks (CNN) framework is constructed to achieve feature extraction, classification and retrieval of images in the encrypted domain. We analyse and evaluate our scheme in terms of both security and efficiency. The results indicate that the proposed secure protocols in our scheme can significantly reduce the computation overhead while protecting the privacy of images as well as the data generated during execution on cloud servers and final results. The performance of our scheme in image feature extraction, classification and retrieval is at a similar level comparable to the scheme based on original CNN in plaintext.

Secure Two-Party Computation Based on Fast Cut-and-Choose Bilateral Oblivious Transfer

In secure two-party computation, each party has its input and wants to jointly compute a function from which it obtains the output corresponding to its respective inputs. For achieving security against a malicious adversary, an effective approach is using cut-and-choose, which requires the circuit constructor P1 to construct S copies of the circuit C (C is used to compute the function F). The circuit evaluator P2 selects S∕2 circuits to open for the check. If these S∕2 circuits are correctly constructed, P2 assumes that the remaining S∕2 circuits are also correctly constructed and uses the remaining circuits to compute. However, this method introduces significant computational complexity and interactive rounds, mainly due to more circuits that must be used for security purposes and the need for multiple interactions to transmit the keys. In this paper, regarding the issue above, we present a novel secure two-party computation protocol, and it can achieve security against the malicious adversary. Concretely, we still use the idea of cut-and-choose but improve the cut-and-choose oblivious transfer (CCOT) of the usual secure two-party computation protocol into cut-and-choose bilateral oblivious transfer (CCBOT) and propose a variant of it that we call batch single-choice CCBOT, which makes our protocol only needs two rounds of interaction to complete the transmission of all keys and 28Sl of exponentiations. In addition, we use a check mechanism to prevent the case that p1 cheats, but P2 is powerless. Our proposed protocol with an error probability of 2–s of P1 significantly optimizes the communication rounds and computation overheads, solves the selective failure attack, and ensures the consistency of the input.

LPCP: An efficient Privacy-Preserving Protocol for Polynomial Calculation Based on CRT

With the development of privacy-preserving techniques, the increasing demand for secure multiparty computation (MPC) of mobile devices has become a significant challenge. Unfortunately, it is inapplicable for mobile devices to implement the existing secure multiparty computation schemes that rely on costly computation and communication overhead. To solve this problem, we propose an efficient two-party computation protocol secure against semi-honest adversary based on the Chinese remainder theorem (CRT). Our protocol utilizes CRT-based encryption and re-encryption techniques to realize additional and multiplicative homomorphic encryption, which can be transformed into a two-party secure computation scheme. Then, we extend our two-party LPCP protocol into a multiparty LPCP protocol, which is much faster and more space saving than the previous works. For practical purpose, we describe a distance measurement application for mobile devices based on LPCP. In the end, we implement LPCP codes and compare the experimental results to the state-of-the-art two-party and multiparty computation protocols. The experimental result shows that the high computation and communication efficiency of LPCP makes it possible for low computing-power mobile devices to implement multiparty secure computation protocols in reality.

Dispelling myths on superposition attacks: formal security model and attack analyses

With the emergence of quantum communication, it is of folkloric belief that the security of classical cryptographic protocols is automatically broken if the Adversary is allowed to perform superposition queries and the honest players forced to perform actions coherently on quantum states. Another widely held intuition is that enforcing measurements on the exchanged messages is enough to protect protocols from these attacks. However, the reality is much more complex. Security models dealing with superposition attacks only consider unconditional security. Conversely, security models considering computational security assume that all supposedly classical messages are measured, which forbids by construction the analysis of superposition attacks. To fill in the gap between those models, Boneh and Zhandry have started to study the quantum computational security for classical primitives in their seminal work at Crypto’13, but only in the single-party setting. To the best of our knowledge, an equivalent model in the multiparty setting is still missing. In this work, we propose the first computational security model considering superposition attacks for multiparty protocols. We show that our new security model is satisfiable by proving the security of the well-known One-Time-Pad protocol and give an attack on a variant of the equally reputable Yao Protocol for Secure Two-Party Computations. The post-mortem of this attack reveals the precise points of failure, yielding highly counter-intuitive results: Adding extra classical communication, which is harmless for classical security, can make the protocol become subject to superposition attacks. We use this newly imparted knowledge to construct the first concrete protocol for Secure Two-Party Computation that is resistant to superposition attacks. Our results show that there is no straightforward answer to provide for either the vulnerabilities of classical protocols to superposition attacks or the adapted countermeasures.

Gradual GRAM and secure computation for RAM programs1

Despite the fact that the majority of applications encountered in practice today are captured more efficiently by RAM programs, the area of secure two-party computation (2PC) has seen tremendous improvement mostly for Boolean circuits. One of the most studied objects in this domain is garbled circuits. Analogously, garbled RAM (GRAM) provide similar security guarantees for RAM programs with applications to constant round 2PC. In this work we consider the notion of gradual GRAM which requires no memory garbling algorithm. Our approach provides several qualitative advantages over prior works due to the conceptual similarity to the analogue garbling mechanism for Boolean circuits. We next revisit the GRAM construction from (In STOC (2015) 449–458) and improve it in two orthogonal aspects: match it directly with tree-based ORAMs and explore its consistency with gradual ORAM.

Privacy-preserving kNN query processing algorithms via secure two-party computation over encrypted database in cloud computing

Since studies on privacy-preserving database outsourcing have been spotlighted in a cloud computing, databases need to be encrypted before being outsourced to the cloud. Therefore, a couple of privacy-preserving kNN query processing algorithms have been proposed over the encrypted database. However, the existing algorithms are either insecure or inefficient. Therefore, in this paper we propose a privacy-preserving kNN query processing algorithm via secure two-party computation on the encrypted database. Our algorithm preserves both data privacy and query privacy while hiding data access patterns. For this, we propose efficient and secure protocols based on Yao’s garbled circuit. To achieve a high degree of efficiency in query processing, we also propose a parallel kNN query processing algorithm using encrypted random value pool. Through our performance analysis, we verify that our proposed algorithms outperform the existing ones in terms of a query processing cost.

Equivocating Yao: Constant-Round Adaptively Secure Multiparty Computation in the Plain Model

Yao's circuit garbling scheme is one of the basic building blocks of cryptographic protocol design. Originally designed to enable two-message, two-party secure computation, the scheme has been extended in many ways and has innumerable applications. Still, a basic question has remained open throughout the years: Can the scheme be extended to guarantee security in the face of an adversary that corrupts both parties, adaptively, as the computation proceeds? We provide a positive answer to this question. We define a new type of encryption, called functionally equivocal encryption (FEE), and show that when Yao's scheme is implemented with an FEE as the underlying encryption mechanism, it becomes secure against such adaptive adversaries. We then show how to implement FEE from any one way function. Combining our scheme with non-committing encryption, we obtain the first two-message, two-party computation protocol, and the first constant-rounds multiparty computation protocol, in the plain model, that are secure against semi-honest adversaries who can adaptively corrupt all parties. A number of extensions and applications are described within.