Forecasting Run-Times of Secure Two-Party Computation

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

Secure multi-party computation (SMC) is a cryptographic protocol that allows participants to compute the desired output without revealing their inputs. A variety of results related to increasing the efficiency of SMC protocol have been reported, and thus, SMC can be used in various applications. With the SMC protocol in smart grids, it becomes possible to obtain information for load balancing and various statistics, without revealing sensitive user information. To prevent malicious users from tampering with input values, SMC requires cheater detection. Several studies have been conducted on SMC with cheater detection, but none of these has been able to guarantee the fairness of the protocol. In such cases, only a malicious user can obtain a correct output prior to detection. This can be a critical problem if the result of the computation is real-time information of considerable economic value. In this paper, we propose a fair and secure multi-party computation protocol, which detects malicious parties participating in the protocol before computing the final output and prevents them from obtaining it. The security of our protocol is proven in the universal composability framework. Furthermore, we develop an enhanced version of the protocol that is more efficient when computing an average after detecting cheaters. We apply the proposed protocols to a smart grid as an application and analyze their efficiency in terms of computational cost.

Secure Computation (SC) enables secure distributed computation of arbitrary functions of private inputs. It has many useful applications, e.g. benchmarking or auctions. Several general protocols for SC have been proposed and recently been implemented in a number of compilers and frameworks. These compilers or frameworks implement one general SC protocol and then require the programmer to implement the function he wants the protocol to compute. Performance remains a challenge for this approach and it has been realized early on that special protocols for important problems can deliver superior performance. In this paper we propose a new intermediate language (L1) for optimizing SC compilers which enables efficient implementation of special protocols potentially mixing several general SC protocols. We show by three case studies - one for computation of the median, one for weighted average, one for division - that special protocols and mixed-protocol implementations in our language L1 can lead to superior performance. Moreover, we show that only a combined view on algorithm and cryptographic protocol can discover SCs with best run-time performance.

Secure multi-party computations for privacy-preserving machine learning

This review chapter provides an in-depth exploration of various aspects of data security, ranging from foundational principles to advanced techniques. It sheds light on the importance of maintaining confidentiality, integrity, and availability of data and presents a comprehensive overview of technologies and methodologies that can be employed to achieve robust data security. In today's digital age, ensuring the security and protection of data has become paramount. This research chapter explores various topics related to data security, including confidentiality, integrity, and availability. It also delves into the role of blockchain technology, zero-trust architecture, secure coding practices, legal and ethical considerations, homomorphic encryption, secure multi-party computation, hash functions and digital signatures, secure communication protocols, cryptocurrency and blockchain security, as well as cryptanalysis and side-channel attacks.It highlights various techniques and technologies that can be employed to protect data confidentiality, including encryption and access control mechanisms. Blockchain technology has emerged as a groundbreaking solution for enhancing data security. Zero-trust architecture challenges traditional security models by adopting a holistic and dynamic approach to data protection. Secure coding practices and DevSecOps play a vital role in developing secure software applications. The chapter investigates the importance of integrating security measures throughout the software development lifecycle. It emphasizes the adoption of secure coding practices and the implementation of DevSecOps methodologies to build resilient and secure software systems. Legal and ethical considerations are crucial when addressing data security. Homomorphic encryption and secure multi-party computation are advanced cryptographic techniques that allow data to be processed securely while encrypted. The chapter examines the principles and applications of these techniques, highlighting their significance in scenarios where privacy is paramount. Hash functions and digital signatures form the backbone of data authentication and integrity. The chapter discusses the properties and applications of hash functions and digital signatures in verifying the integrity and authenticity of data. Secure communication protocols are essential for protecting data during transmission. The chapter explores various secure communication protocols, such as Transport Layer Security (TLS), and examines their role in ensuring data confidentiality, integrity, and authenticity. Cryptocurrency and blockchain security focus on the secure storage and transfer of digital assets. The research chapter investigates the security considerations specific to cryptocurrencies and explores how block chain technology can mitigate vulnerabilities and ensure the trustworthiness of transactions. Cryptanalysis and side-channel attacks are threats to cryptographic systems. The chapter discusses these attack techniques, highlighting their potential risks and the measures that can be taken to counter them.

Secure multi-party computation (MPC) allows two or more distrusting parties to jointly evaluate a function over private inputs. For a long time considered to be a purely theoretical concept, MPC transitioned into a practical and powerful tool to build privacy-enhancing technologies. However, the practicality of MPC is hindered by the difficulty to implement applications on top of the underlying cryptographic protocols. This is because the manual construction of efficient applications, which need to be represented as Boolean or arithmetic circuits, is a complex, error-prone, and time-consuming task. To facilitate the development of further privacy-enhancing technology, multiple compilers have been proposed that create circuits for MPC. Yet, almost all presented compilers only support domain specific languages or provide very limited optimization methods. In this work (this is an extended and revised version of the paper ‘Secure Two-party Computations in ANSI C’ (Holzer et al., in: ACM CCS, 2012) that reflects the progress in secure computation and describes the current optimization tool chain of CBMC-GC) we describe our compiler CBMC-GC that implements a complete tool chain from ANSI C to circuit. Moreover, we give a comprehensive overview of circuit minimization techniques, which we have identified and adapted for the creation of efficient circuits for MPC. With the help of these techniques, our compilation approach allows for a high level of abstraction from the cryptographic primitives used in MPC protocols, as well as the complex design of digital circuits. By using the model checker CBMC as a compiler frontend, we illustrate the link between MPC, formal methods, and digital logic design. Our experimental results illustrate the effectiveness of the implemented optimizations techniques for various example applications. In particular, compared with other state-of-the-art compilers, we show that CBMC-GC compiles circuits from the same source code that are up to four times smaller.

This paper discusses the decision problem concerning security for such cryptographic protocols as the cryptographic key distribution protocols and the digital signature protocols. A cryptographic protocol and its utilization environment are considered secure if an opponent cannot know the secret message or forge a signature successfully by wiretapping or communication function is secure. This paper presents a sufficient condition under which the security problem is decidable. Dolev et al. also investigated the decision problem of the security, but they discussed only the simple case where the cryptographic key is not the object of manipulation (i.e., key generation and distribution are not included in the protocol), and a decision method is presented in which the decision can be made in a polynomial time. By contrast, this paper discusses the problem including the case where the cryptographic key can be an object of manipulation. The key delivery protocol by Ehrsam et al. and the digital signature protocol by Needham et al. satisfy the sufficient condition for the decidability given in this paper. Under the sufficient condition given in this paper, the security can again be decided in a polynomial time.

Quantum computing has the capability of parallel computing and is superior to classical computing in solving some specific problems. Once a large-scale quantum computer is developed, the security of classical cryptographic algorithms and protocols, which is based on the assumption of computational complexity, will be severely challenged. Quantum cryptography is a new cryptosystem; its security is based on the principles of quantum mechanics, and can resist the attack of quantum computing. This paper focuses on the nearly 40 years development of quantum cryptographic protocols, including quantum key distribution (QKD), quantum secure direct communication, quantum secret sharing, quantum identity authentication, two-party secure computation, and quantum private query, and summarizes the problems in the process of development. The analysis shows that the quantum cryptographic protocols are in an unbalanced state: QKD is far ahead of other protocols and other protocols are difficult to achieve breakthroughs. In the future, practical quantum protocols for digital signature and two-party secure computation are core issues that needs to be addressed urgently. Therefore, research on quantum and post-quantum cryptography should be conducted synchronously, cross-over study and talent cultivation for the quantum science and cryptography disciplines should be strengthened, and the examination and evaluation mechanism of relevant basic research needs to be optimized.

In this paper, we introduce secure groups as a cryptographic scheme representing finite groups together with a range of operations, including the group operation, inversion, random sampling, and encoding/decoding maps. We construct secure groups from oblivious group representations combined with cryptographic protocols, implementing the operations securely. We present both generic and specific constructions, in the latter case specifically for number-theoretic groups commonly used in cryptography. These include Schnorr groups (with quadratic residues as a special case), Weierstrass and Edwards elliptic curve groups, and class groups of imaginary quadratic number fields. For concreteness, we develop our protocols in the setting of secure multiparty computation based on Shamir secret sharing over a finite field, abstracted away by formulating our solutions in terms of an arithmetic black box for secure finite field arithmetic or for secure integer arithmetic. Secure finite field arithmetic suffices for many groups, including Schnorr groups and elliptic curve groups. For class groups, we need secure integer arithmetic to implement Shanks’ classical algorithms for the composition of binary quadratic forms, which we will combine with our adaptation of a particular form reduction algorithm due to Agarwal and Frandsen. As a main result of independent interest, we also present an efficient protocol for the secure computation of the extended greatest common divisor. The protocol is based on Bernstein and Yang’s constant-time 2-adic algorithm, which we adapt to work purely over the integers. This yields a much better approach for multiparty computation but raises a new concern about the growth of the Bézout coefficients. By a careful analysis, we are able to prove that the Bézout coefficients in our protocol will never exceed 3max(a,b) in absolute value for inputs a and b. We have integrated secure groups in the Python package MPyC and have implemented threshold ElGamal and threshold DSA in terms of secure groups. We also mention how our results support verifiable multiparty computation, allowing parties to jointly create a publicly verifiable proof of correctness for the results accompanying the results of a secure computation.

In the specific value computation scenario of blockchain cross-chain data exchange, participants can organize raw data in the form of data pairs, making the computed specific values become key information for cross-chain collaboration. The main challenges faced in this scenario include: the lack of appropriate privacy protection mechanisms leading to easy leakage of sensitive data; the vulnerability of unencrypted or unvalidated data to tampering or malicious attacks; a crisis of trust among users. To address the above problems, this paper introduces secure multi-party computation (MPC) into the cross-chain data exchange process to ensure the fairness of the computation process and protect data privacy. To address the problem of computing the maximum and minimum values of the sums of keyword-corresponding values in cross-chain interactions, it is transformed into the secure computation scheme of computing the maximum and minimum values of the sums of corresponding elements in the intersection of sets (MMSI). A secure computation scheme for maximum and minimum values based on the fully homomorphic NTRU (FH-NTRU) encryption algorithm is proposed. First, a secure computation protocol for MMSI without a universal set under the semi-honest model is proposed. Then, to address potential malicious behaviors in the protocol, a secure computation protocol for MMSI under the malicious model is proposed, using the cut-and-choose method. The protocol under the malicious model is analyzed for correctness and the security of the protocol is proved using the real/ideal model paradigm. Finally, the efficiency analysis and experimental simulations show that the protocol is efficient and reliable, it can resist malicious adversary attacks and ensure the correctness of the computation results while effectively improving the security during cross-chain interactions.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-14629-1.

We have developed a framework for efficient privacy preserving multi-party querying (PPMQ) over federated graph databases, leveraging Secure Multi-Party Computation (SMPC) protocols to enhance data security. The system offers two distinct security protocols: a client-based protocol and a server-based protocol. In the client-based protocol, standard SMPC techniques are employed, allowing computations to be performed on data without exposing the data itself. The server-based protocol employs SMPC to facilitate secure data processing and is further enhanced by encrypted hashing, which adds an additional layer of security to prevent data exposure. We conducted experiments comparing PPMQ with Neo4j Fabric and two previous systems, SMPQ and Conclave. The results indicate that PPMQ’s execution times and overheads are comparable to those of Neo4j Fabric, while outperforming both SMPQ and Conclave, demonstrating its superior efficiency. Additionally, PPMQ, like SMPQ and Conclave, utilises an honest but curious security model. However, it enhances the security of the server protocol, making it more robust against brute force attacks and providing stronger privacy guarantees than previous solutions.

MKSS: An Effective Multi-authority Keyword Search Scheme for edge–cloud collaboration

Secure Multi-party Computation (SMC) is dedicated to solve trust problems in cooperative computing with each participant’s private data. Privacy Preserving Computational Geometry (PPCG) is a special area in SMC and is being widely used. This paper investigates the problem of judging whether a point is in a convex hull or not. We firstly present a secure protocol to determine the position of a private point and a private line, based on which two efficient point-inclusion schemes are proposed. Compared with previous solutions, our protocols perform better in efficiency, especially when the number of the convex hull’s points is large.

The integration of Internet of Things (IoT) technologies within autonomous vehicles (AVs) is revolutionizing modern transportation by enabling intelligent, connected, and self-operating mobility systems. However, this digital interconnectivity significantly expands the attack surface, exposing vehicular networks to sophisticated cybersecurity threats that could compromise safety, data privacy, and operational integrity. This chapter presents a comprehensive examination of cybersecurity architectures tailored for IoT-enabled autonomous vehicles, with a focus on identifying prevalent attack vectors, including denial-of-service (DoS/DDoS), spoofing, and data injection threats. The discourse extends to secure microcontroller and SoC designs, cryptographic protocols, authentication mechanisms, and privacy-preserving techniques such as homomorphic encryption and secure multiparty computation. Additionally, the chapter explores the role of intrusion detection and prevention systems (IDPS) and collaborative threat intelligence frameworks in fortifying AV ecosystems. Emerging challenges in secure key management, system scalability, and real-time performance are critically analyzed. The chapter concludes with strategic future directions and architectural recommendations to foster resilient, privacy-aware, and sustainable AV cybersecurity infrastructures. This work contributes to advancing secure intelligent mobility within the broader paradigm of smart urban transportation.

Problems of secure communication and computation have been studied extensively in network models. Goldreich, Goldwasser, and Linial, Franklin and Yung, and Franklin and Wright have initiated the study of secure communication and secure computation in multi-recipient (broadcast) models. A “broadcast channel” (such as ethernet) enables one processor to send the same message—simultaneously and privately—to a fixed subset of processors. In their Eurocrypt ’98 paper, Franklin and Wright have shown that if there are n broadcast lines between a sender and a receiver and there are at most t malicious (Byzantine style) processors, then the condition n > t is necessary and sufficient for achieving efficient probabilisticly reliable and probabilisticly private communication. They also showed that if n > [3t=2] then there is an efficient protocol to achieve probabilisticly reliable and perfectly private communication. And they left open the question whether there exists an efficient protocol to achieve probabilisticly reliable and perfectly private communication when [3t=2] ≥ n > t. In this paper, by using a different authentication scheme, we will answer this question affirmatively and study related problems.KeywordsNetwork securityPrivacyPerfect secrecyReliability