Secure Computation Research Articles

Privacy preserving image registration

Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image registration are generally based on the assumption that the content of the images is usually accessible in clear form, from which the spatial transformation is subsequently estimated. This common assumption may not be met in practical applications, since the sensitive nature of medical images may ultimately require their analysis under privacy constraints, preventing to openly share the image content. In this work, we formulate the problem of image registration under a privacy preserving regime, where images are assumed to be confidential and cannot be disclosed in clear. We derive our privacy preserving image registration framework by extending classical registration paradigms to account for advanced cryptographic tools, such as secure multi-party computation and homomorphic encryption, that enable the execution of operations without leaking the underlying data. To overcome the problem of performance and scalability of cryptographic tools in high dimensions, we propose several techniques to optimize the image registration operations by using gradient approximations, and by revisiting the use of homomorphic encryption trough packing, to allow the efficient encryption and multiplication of large matrices. We focus on registration methods of increasing complexity, including rigid, affine, and non-linear registration based on cubic splines or diffeomorphisms parameterized by time-varying velocity fields. In all these settings, we demonstrate how the registration problem can be naturally adapted for accounting to privacy-preserving operations, and illustrate the effectiveness of PPIR on a variety of registration tasks.

Custody in the age of digital assets : The path to building market infrastructure fit for a tokenised economy

This paper charts the dynamic evolution of finance and capital markets amid the digital asset surge. Tracing the historical trajectory from conventional banking to the current digital era, the paper underscores the intertwined narrative of finance and technology. Distributed ledger technology (DLT) and digital assets take centre stage, driving continuous financial operations and reshaping market dynamics. Custody emerges as a pivotal factor in this digital transformation. Established financial institutions strategically invest in digital asset infrastructure, recognising the seismic potential of tokenisation. Research from major companies projects significant tokenisation of assets, prompting global banks to align their strategies accordingly. The paper explores diverse use cases, from custody and trading services for cryptocurrencies to tokenising financial securities and real-world assets. Navigating the regulatory landscape proves crucial, with varying regulations across jurisdictions posing challenges. A unified regulatory framework is advocated for fostering global adoption. The paper delves into the technological nuances of safeguarding digital assets, exploring key management solutions such as multiparty computation (MPC) and hardware security modules (HSMs). The paper anticipates a shift towards recentralisation as the digital asset market matures, with established financial institutions and custodians poised to leverage trust, balance sheets and regulatory compliance to open up commercial opportunities in the digital asset space. In essence, the paper provides a sweeping overview of the current digital asset landscape, offering insights into challenges, opportunities and technological considerations shaping the digital asset custody value chain.

Hybrid Edge Detection Methods in Image Steganography for High Embedding Capacity

Abstract In this research, we propose two new image steganography techniques focusing on increasing image-embedding capacity. The methods will encrypt and hide secret information in the edge area. We utilized two hybrid methods for the edge detection of the images. The first method combines the Laplacian of Gaussian (LoG) with the wavelet transform algorithm and the second method mixes the LOG and Canny. The Combining was performed using addWeighted. The text message will be encrypted using the GIFT cipher method for further security and low computation. For the effectiveness evaluation of the proposed method, various evaluation metrics were used such as embedding capacity, PSNR, MSE, and SSIM. The obtained results indicate that the proposed method has a greater embedding capacity in comparison with other methods, while still maintaining high levels of imperceptibility in the cover image.

Practical solutions in fully homomorphic encryption: a survey analyzing existing acceleration methods

Fully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve privacy. Nonetheless, the application of FHE is constrained by its substantial computing overhead and storage cost. Researchers have proposed practical acceleration solutions to address these issues. This paper aims to provide a comprehensive survey for systematically comparing and analyzing the strengths and weaknesses of FHE acceleration schemes, which is currently lacking in the literature. The relevant researches conducted between 2019 and 2022 are investigated. We first provide a comprehensive summary of the latest research findings on accelerating FHE, aiming to offer valuable insights for researchers interested in FHE acceleration. Secondly, we classify existing acceleration schemes from algorithmic and hardware perspectives. We also propose evaluation metrics and conduct a detailed comparison of various methods. Finally, our study presents the future research directions of FHE acceleration, and also offers both guidance and support for practical application and theoretical research in this field.

Enhancing Collaborative Intelligence: Synergistic Approaches for Distributed Machine Learning and Ai Collaboration in Heterogeneous Environments

Collaborative intelligence is vital in distributed machine learning and AI collaboration, especially in heterogeneous environments. This paper explores synergistic approaches to enhance collaborative intelligence by addressing challenges in communication, privacy, resource optimization, domain adaptation, and scalability. The paper reviews existing techniques and methodologies in the field of collaborative intelligence. It discusses protocols, coordination strategies, and communication mechanisms for effective collaboration. Privacy-preserving techniques, such as federated learning and secure multi-party computation, are examined. Resource optimization techniques, including load balancing and adaptive resource allocation, are explored. Domain adaptation and transfer learning methods are also discussed.

Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance

In the contemporary landscape of big data analytics, privacy concerns loom large, exacerbated by escalating surveillance measures. This review delves into the advancements of privacy-enhancing technologies (PETs) amidst this era of heightened scrutiny. The review explores the evolving landscape of PETs, highlighting their pivotal role in safeguarding individual privacy while enabling meaningful data analysis. Firstly, the review elucidates the escalating surveillance environment, characterized by ubiquitous data collection practices and the proliferation of sophisticated monitoring mechanisms. Against this backdrop, the imperative for robust privacy solutions becomes evident. Subsequently, the review navigates through the array of PETs, encompassing differential privacy, homomorphic encryption, secure multi-party computation, and federated learning, among others. Each technology is scrutinized for its efficacy in mitigating privacy risks without compromising analytical utility. Furthermore, the review delineates notable applications of PETs across diverse domains, including healthcare, finance, and social media. Case studies exemplify how PETs facilitate data sharing and collaborative analysis while preserving confidentiality and compliance with regulatory frameworks. Moreover, the review examines the challenges hindering the widespread adoption of PETs, such as computational overhead, interoperability issues, and regulatory ambiguities. Strategies for overcoming these hurdles are elucidated, encompassing advancements in algorithmic efficiency, standardization efforts, and policy advocacy. The review underscores the pivotal role of PETs in reconciling the imperatives of data analytics with the imperatives of privacy protection amidst escalating surveillance. It emphasizes the necessity for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to foster the development and deployment of effective PET solutions, thereby ensuring a harmonious balance between data utility and individual privacy rights in the digital age.

Practical quantum protocols for blind millionaires’ problem based on rotation encryption and swap test

Millionaires’ problem, as the most fundamental problem in secure multiparty computation (SMC), has attracted much attention in recent years. Blind millionaires’ problem, an extension of millionaires’ problem, enables to determine the size relationship of the secrets sum. In this paper, two practical quantum protocols based on rotation encryption and swap test are proposed, which can essentially solve the blind millionaires’ problem. To verify the correctness and feasibility, the proposed protocols are simulated on IBM Quantum Platform by designing the corresponding quantum circuits. Compared with the existing quantum solutions for the blind millionaires’ problem, the proposed protocols demonstrate improved performance in terms of feasibility and security, as they utilize Bell states, single-particle measurements, rotation operations and swap tests without necessitating the preparation of d-dimensional quantum states, the shift operation of d-dimensional quantum states, quantum Fourier transform, or the measurement of computational basis and Fourier basis. Security analysis demonstrates that the private data of each participant and the secrets sum remain confidential and undisclosed.

Implementing quantum anonymous multi-party ranking: the influence and application of the law of large numbers

Secure multi-party ranking is not only a pivotal component within the domain of secure multi-party computation but also holds extensive real-world applicability, given its efficacy in safeguarding the integrity and privacy of data. In this paper, we propose a verifiable quantum anonymous multi-party ranking protocol, which leverages the power of multi-particle entangled states in conjunction with the law of large numbers. The protocol achieves the secure ranking of participants’ private data while verifying their identities through the assistance of a semi-honest third party. We prove that the protocol is resistant to different types of attacks from internal or external attackers. In addition, the proposed protocol employs qubits as the information carriers, which improves the feasibility of the protocol. We demonstrate the feasibility of the protocol by using the online analog quantum computer of IBM Corporation placed on the cloud. Our research represents an innovative initiative that merges the realms of quantum cryptography and statistical analysis to address pertinent practical challenges.

Two‐Server Oblivious Transfer for Quantum Messages

AbstractOblivious transfer is considered as a cryptographic primitive task for quantum information processing over a quantum network. It is an essential building block for secure multiparty computation. It is known that one‐server oblivious transfer is impossible. When the task is the transmission of classical messages, protocols for two‐server oblivious transfer exist, i.e., existing protocols work under the assumption that two servers do not communicate with each other. However, when the task is the transmission of a quantum state, no existing method works even under the above two‐server assumption. Two‐server oblivious transfer protocols for quantum messages are proposed for the first time.

Prioritization and exchange chains in privacy-preserving kidney exchange

The Kidney Exchange Problem (KEP) aims at finding an optimal set of exchanges among pairs of patients and their medically incompatible living kidney donors as well as altruistic donors who are not associated with any particular patient but want to donate a kidney to any person in need. Existing platforms that offer the finding of such exchanges for patient-donor pairs and altruistic donors are organized in a centralized fashion and operated by a single platform operator. This makes them susceptible to manipulation and corruption. Recent research has targeted these security issues by proposing decentralized Secure Multi-Party Computation (SMPC) protocols for solving the KEP. However, these protocols fail to meet two important requirements for kidney exchange in practice. First, they do not allow for altruistic donors. While such donors are not legally allowed in all countries, they have been shown to have a positive effect on the number of transplants that can be found. Second, the existing SMPC protocols do not support prioritization, which is used in existing platforms to give priority to certain exchanges or patient-donor pairs, e.g., to patients who are hard to match due to their medical characteristics. In this paper, we introduce a generic gate for implementing prioritization in kidney exchange. We extend two existing SMPC protocols for solving the KEP such that they allow for altruistic donors and prioritization and present one novel SMPC protocol for solving the KEP with altruistic donors and prioritization based on dynamic programming. We prove the security of all protocols and analyze their complexity. We implement all protocols and evaluate their performance for the setting where altruistic donors are legally allowed and for the setting where they are not. Thereby, we determine the performance impact of the inclusion of altruistic donors and obtain those approaches that perform best for each setting.

Sphinx-in-the-Head: Group Signatures from Symmetric Primitives

Group signatures and their variants have been widely used in privacy-sensitive scenarios such as anonymous authentication and attestation. In this paper, we present a new post-quantum group signature scheme from symmetric primitives. Using only symmetric primitives makes the scheme less prone to unknown attacks than basing the design on newly proposed hard problems whose security is less well-understood. However, symmetric primitives do not have rich algebraic properties, and this makes it extremely challenging to design a group signature scheme on top of them. It is even more challenging if we want a group signature scheme suitable for real-world applications, one that can support large groups and require few trust assumptions. Our scheme is based on MPC-in-the-head non-interactive zero-knowledge proofs, and we specifically design a novel hash-based group credential scheme, which is rooted in the SPHINCS+ signature scheme but with various modifications to make it MPC (multi-party computation) friendly. The security of the scheme has been proved under the fully dynamic group signature model. We provide an implementation of the scheme and demonstrate the feasibility of handling a group size as large as 2 60 . This is the first group signature scheme from symmetric primitives that supports such a large group size and meets all the security requirements.

Extremely Efficient and Privacy-Preserving MAX/MIN Protocol Based on Multiparty Computation in Big Data

Extremely Efficient and Privacy-Preserving MAX/MIN Protocol Based on Multiparty Computation in Big Data

Secure Supervised Learning-Based Smart Home Authentication Framework

The Smart home possesses the capability of facilitating home services to their users with the systematic advance in The Internet of Things (IoT) and information and communication technologies (ICT) in recent decades. The home service offered by the smart devices helps the users in utilize maximized level of comfort for the objective of improving life quality. As the user and smart devices communicate through an insecure channel, the smart home environment is prone to security and privacy problems. A secure authentication protocol needs to be established between the smart devices and the user, such that a situation for device authentication can be made feasible in smart home environments. Most of the existing smart home authentication protocols were identified to fail in facilitating a secure mutual authentication and increases the possibility of lunching the attacks of session key disclosure, impersonation and stolen smart device. In this paper, Secure Supervised Learning-based Smart Home Authentication Framework (SSL-SHAF) is proposed as are liable mutual authentication that can be contextually imposed for better security. The formal analysis of the proposed SSL-SHAF confirmed better resistance against session key disclosure, impersonation and stolen smart device attacks. The results of SSL-SHAF confirmed minimized computational costs and security compared to the baseline protocols considered for investigation.

Temporal ECDSA: A timestamp and signature mask enabled ECDSA algorithm for IoT client node authentication

Internet of Things (IoT) networks are vulnerable to various security threats, especially in client authentication. The current authentication methods require significant resources and are vulnerable to specific attacks. The Non-Interactive Zero Knowledge Proof (NIZKP) concept suits IoT device authentication. Elliptic Curve Digital Signature (ECDSA) is a popular algorithm for IoT device authentication based on elliptic curve cryptography (ECC) and NIZKP. However, an intelligent attacker who captures the ECDSA signatures generated by the same random number can recover the private key. This paper proposes a timestamp based approach to prevent signature reuse and fake signature generation in the ECDSA algorithm. The signature proof is generated differently at each occurrence to prevent the generation of valid signatures from leaked private keys. The proposed approach eliminates the expensive modular inversion operations in signature generation and verification phases, enhancing execution efficiency. The analysis results indicate that the proposed Temporal ECDSA algorithm effectively prevents most attacks on client authentication in IoT networks. Various metrics, including computational complexity and security measures, were used to evaluate the effectiveness of the proposed approach, demonstrating that it outperforms most ECDSA variants.

Cloud-SMPC: two-round multilinear maps secure multiparty computation based on LWE assumption

Cloud computing has data leakage from all parties, security protection of private data, and existing solutions do not provide a trade-off between security and overhead. With distributed data communication due to data barriers, information interaction security and data computation security have become challenges for secure computing. Combining cloud computing with secure multiparty computation can provide a higher level of data protection while maintaining the benefits of cloud computing. In this case, data can be stored in the cloud and computed through SMPC protocols, thus protecting the privacy and security of the data. However, multiple rounds of information interaction are often required, increasing the communication overhead, and the security strength is limited by the hardness assumption. In this paper, we work to achieve an optimal setting of the number of rounds in secure multi-party computation on the cloud to achieve a sublinear communication overhead and improve the security concept. A 2-round SMPC protocol is constructed in the framework of Universally Composable (UC). A 2-round SMPC protocol is constructed that uses multilinear maps based on the Learning from Errors (LWE) assumption. The participant encodes the input and sends it via broadcast to reduce the interaction, homomorphic computational encoding information for secure access to computational data and secure the SMPC protocol through UC security. This paper extends the participants to multiple parties, reduces the communication rounds to 2, the protocol achieves sublinear communication overhead in poly polynomial time, smaller setup size to poly(k), and static security is achieved.

Quantum Secure Multi-Party Summation with Graph State.

Quantum secure multi-party summation (QSMS) is a fundamental problem in quantum secure multi-party computation (QSMC), wherein multiple parties compute the sum of their data without revealing them. This paper proposes a novel QSMS protocol based on graph state, which offers enhanced security, usability, and flexibility compared to existing methods. The protocol leverages the structural advantages of graph state and employs random graph state structures and random encryption gate operations to provide stronger security. Additionally, the stabilizer of the graph state is utilized to detect eavesdroppers and channel noise without the need for decoy bits. The protocol allows for the arbitrary addition and deletion of participants, enabling greater flexibility. Experimental verification is conducted to demonstrate the security, effectiveness, and practicality of the proposed protocols. The correctness and security of the protocols are formally proven. The QSMS method based on graph state introduces new opportunities for QSMC. It highlights the potential of leveraging quantum graph state technology to securely and efficiently solve various multi-party computation problems.

Incentive Mechanism for Privacy-Preserving Collaborative Routing Using Secure Multi-Party Computation and Blockchain.

Traffic congestion results from the spatio-temporal imbalance of demand and supply. With the advances in connected technologies, incentive mechanisms for collaborative routing have the potential to provide behavior-consistent solutions to traffic congestion. However, such mechanisms raise privacy concerns due to their information-sharing and execution-validation procedures. This study leverages secure Multi-party Computation (MPC) and blockchain technologies to propose a privacy-preserving incentive mechanism for collaborative routing in a vehicle-to-everything (V2X) context, which consists of a collaborative routing scheme and a route validation scheme. In the collaborative routing scheme, sensitive information is shared through an off-chain MPC protocol for route updating and incentive computation. The incentives are then temporarily frozen in a series of cascading multi-signature wallets in case vehicles behave dishonestly or roadside units (RSUs) are hacked. The route validation scheme requires vehicles to create position proofs at checkpoints along their selected routes with the assistance of witness vehicles using an off-chain threshold signature protocol. RSUs will validate the position proofs, store them on the blockchain, and unfreeze the associated incentives. The privacy and security analysis illustrates the scheme's efficacy. Numerical studies reveal that the proposed incentive mechanism with tuned parameters is both efficient and implementable.

Privacy-preserving recommendation system based on social relationships

In the era of internet-based data, recommendation systems are crucial for helping users access personalized content and facilitating business promotion and sales. Recommendation systems based on social relationships have gained popularity due to their ability to use social influence and enhance the impact and credibility of recommendations. However, building a successful recommendation system faces the challenge of protecting user data privacy, as it requires a significant amount of sensitive user data. For this challenge, we propose a privacy-preserving recommendation system based on social relationships in the dual-cloud model. To achieve the secure computation of this system, we also design and improve some underlying secure protocols such as secure equality protocol and secure comparison protocol. Our underlying protocol has the advantages of high efficiency and provable security. As a result, our recommendation system can provide excellent recommendations while ensuring privacy protection. Experiments show that our system can perform secure recommendation calculations on 128-bit data with semi-honest security in approximately 5.5 s.

Pakistan in the Crosshairs and the Rising Stakes of Strategic Information Warfare

Over the past two decades, information and communication technologies (ICTs) have reaped significant attention from security experts, computer scientists, intelligence agencies, and governments for their potential applications in conflict scenarios. Using ICTs for offensive or defensive purposes to instantly invade, disrupt, or control the opponent's resources is known as strategic information warfare (SIW). Although SIW is as ancient as military history, the communication sciences revolution has altered its character. It has evolved into a double-edged weapon that is equally crucial for strong nations, technically underdeveloped states, non-state entities, and individual software experts. Some nations, most notably the US, Russia, and China, have taken the SIW technology seriously and compared it to the danger posed by WMDs. In the south Asian context, Pakistan faces a direct threat from India, which collaborates closely with Israel, by the use of SIW.
 Additionally, the Western media portrays a highly negative picture of Pakistan's administration, law and order, and economy. The national media, suitably supported by foreign players, has begun a propaganda warfare effort against Pakistan's military forces and intelligence institutions. However, SIW is now a global danger, necessitating a worldwide response. Pakistan must create a proactive national strategy to prepare for the problems erupted from SIW strike on the communication network, which is crucial for the operation of its nuclear facilities.

Secure blockchain bidding auction protocol against malicious adversaries

In recent years, with the development of blockchain, electronic bidding auction has received more and more attention. Aiming at the possible problems of privacy leakage in the current electronic bidding and auction, this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries, which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third party. The protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve cryptography. It can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants, which can solve the problem of resisting malicious adversary attacks. The security of the protocol is proved by the real/ideal model paradigm, and the efficiency of the protocol is analyzed. The efficiency of the protocol is verified by simulating experiments, and the protocol has practical value.