Secure Computation Research Articles

Quantum computing integration with multi-cloud architectures: enhancing computational efficiency and security in advanced cloud environments

The objective of this research is to explore the integration of quantum computing with multi-cloud architectures, aiming to enhance computational efficiency and security in advanced cloud environments. The study seeks to identify the potential benefits and challenges of incorporating quantum computing capabilities within a multi-cloud framework and to evaluate the impact on performance and security metrics. The research employs a hybrid methodological approach, combining both theoretical analysis and practical implementation. Initially, a detailed literature review is conducted to understand the current state of quantum computing and multi-cloud architectures. This is followed by the design and development of an integration framework that leverages quantum computing technologies in a multi-cloud environment. Key steps include developing a multi-cloud architecture that integrates quantum computing resources alongside classical computing resources, deploying quantum algorithms and protocols within the multi-cloud setup, implementing advanced security measures to protect data and computational processes, using a set of predefined metrics to evaluate computational efficiency and security, and employing statistical tools and techniques to analyze the collected data and draw meaningful insights. The integration of quantum computing with multi-cloud architectures resulted in significant improvements in computational efficiency, particularly in tasks that are traditionally resource-intensive. Key findings include enhanced computational speed, where quantum algorithms demonstrated superior performance in solving complex problems compared to classical algorithms, optimized resource utilization through dynamic allocation of quantum and classical resources leading to cost efficiency, improved security with quantum-enhanced protocols providing robust protection against cyber threats, and high scalability of the integrated architecture to accommodate increasing computational demands without compromising performance. The research concludes that integrating quantum computing with multi-cloud architectures offers substantial benefits in terms of computational efficiency and security. The findings indicate that such integration can revolutionize cloud computing, providing a powerful platform for handling complex computations and enhancing data security. However, the study also highlights several challenges, including the need for specialized hardware, the complexity of integration, and the necessity for ongoing research to fully harness the potential of quantum computing in cloud environments. Future research should focus on addressing these challenges and exploring further applications of quantum computing in various cloud-based scenarios.

Advanced Encryption Techniques in Healthcare IoT: Securing Patient Data in Connected Medical Devices

As a result of the fast development of Internet of Things (IoT) technology, the healthcare sector has undergone a transformation. This transformation has been brought about by the deployment of linked medical devices that provide immediate monitoring and data collecting. Despite the fact that these innovations promise to bring about considerable gains in patient care and operational efficiency, they also bring about major security issues, especially with regard to the protection of personally identifiable information about patients. Within the context of the Internet of Things (IoT) ecosystem for healthcare, this study investigates sophisticated encryption approaches that are aimed to protect patient data stored in linked medical equipment. One of the first things that the research does is investigate the specific security needs and risks that are linked with Internet of Things (IoT) devices in the healthcare industry. These include concerns around data privacy, integrity, and authentication. Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), and Quantum Key Distribution (QKD) are some of the encryption standards and protocols that are often used in the process of protecting Internet of Things (IoT) connections. This article presents a complete examination of these encryption standards and protocols. Following that, it digs into sophisticated encryption approaches that are especially targeted to the limits and needs of healthcare IoT systems. These techniques include lightweight cryptographic algorithms, key management strategies, and secure multi-party computing (MPC) frameworks.

Secure multi-party computation with legally-enforceable fairness

Fairness is a security notion of secure computation and cannot always be achieved if an adversary corrupts a majority of parties in standard settings. Lindell (CT-RSA 2008) showed that imposing a monetary penalty on an adversary can circumvent the impossibility. He formalized such a security notion as “legally enforceable fairness" for the two-party setting based on the ideal trusted bank functionality and showed a protocol achieving the requirements. Based on the same framework, we introduce secure multi-party computation with legally enforceable fairness that is applicable for an arbitrary number of parties. Further, we propose two protocols that realize our introduced functionality. The first one achieves O(n) rounds and O(nα)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n \\alpha )$$\\end{document} fees, where n is the number of parties, and α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document} is a parameter for the penalty amount. The fee refers to the balance amount in the bank required at the beginning of the protocol, which evaluates the difficulty of participating in the protocol in a financial sense. The second one achieves O(1) rounds and O(n2α)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2 \\alpha )$$\\end{document} fees.

An end-to-end framework for private DGA detection as a service.

Domain Generation Algorithms (DGAs) are used by malware to generate pseudorandom domain names to establish communication between infected bots and command and control servers. While DGAs can be detected by machine learning (ML) models with great accuracy, offering DGA detection as a service raises privacy concerns when requiring network administrators to disclose their DNS traffic to the service provider. The main scientific contribution of this paper is to propose the first end-to-end framework for privacy-preserving classification as a service of domain names into DGA (malicious) or non-DGA (benign) domains. Our framework achieves these goals by carefully designed protocols that combine two privacy-enhancing technologies (PETs), namely secure multi-party computation (MPC) and differential privacy (DP). Through MPC, our framework enables an enterprise network administrator to outsource the problem of classifying a DNS (Domain Name System) domain as DGA or non-DGA to an external organization without revealing any information about the domain name. Moreover, the service provider's ML model used for DGA detection is never revealed to the network administrator. Furthermore, by using DP, we also ensure that the classification result cannot be used to learn information about individual entries of the training data. Finally, we leverage post-training float16 quantization of deep learning models in MPC to achieve efficient, secure DGA detection. We demonstrate that by using quantization achieves a significant speed-up, resulting in a 23% to 42% reduction in inference runtime without reducing accuracy using a three party secure computation protocol tolerating one corruption. Previous solutions are not end-to-end private, do not provide differential privacy guarantees for the model's outputs, and assume that model embeddings are publicly known. Our best protocol in terms of accuracy runs in about 0.22s.

Anonymous voting using distributed ledger-assisted secure multi-party computation

High voter turnout in elections and referendums is desirable to ensure a robust democracy. Secure electronic voting is a vision for the future of elections and referendums. Such a system can counteract factors hindering strong voter turnout such as the requirement of physical presence during limited hours at polling stations. However, this vision brings transparency and confidentiality requirements that render the design of such solutions challenging. Specifically, the counting implementation must support reproducibility, and the choice of individual voters must remain confidential. In this paper, we propose and evaluate a novel referendum protocol that ensures transparency, confidentiality, and integrity, in trustless networks. The protocol is built by combining secure multi-party computation and distributed ledger technology, e.g., a Blockchain. The persistence and immutability of the protocol communication allow verifiability of the referendum outcome by any participant. Voters therefore do not need to trust third parties. We provide a formal description and conduct a thorough security evaluation of our proposal.

A dynamic symmetric key generation at wireless link layer: information-theoretic perspectives

The expansion of wireless communication introduces security vulnerabilities, emphasizing the essential need for secure systems that prioritize confidentiality, integrity, and other key aspects of data protection. Since computational security acknowledges the possibility of breaches when adequate computational resources are available, that is why information-theoretic security is being explored, which suggests the existence of unbreakable cryptographic systems even in the presence of limitless processing power. Secret key exchange has traditionally relied on RSA or DH protocols, but researchers are now exploring innovative approaches for sharing secret keys among wireless network devices, leveraging physical or link layer characteristics. This research seeks to revolutionize secure multi-party key acquisition in wireless networks, capitalizing on information-theoretic security and collaborative data extraction. The proposed secret key generation framework comprehensively organizes and explains the information-theoretic aspects of secret key generation within the lower layers of wireless networks, especially the link layer, proposes a novel information-theoretic SKG framework for the dynamic acquisition of symmetric secret keys, and responds to contemporary information security challenges by relying on information-theory principles rather than vulnerable mathematical relationships in the post-quantum period. A new cryptographic key can be generated using a straightforward method, and when it is combined (XORed) with the previous key, it creates a continuously changing secret for encryption and decryption. This approach enhances security because, as attackers attempt to break the encryption, the system generates fresh, dynamic keys, making it progressively more challenging for them to succeed. The research work in question integrates key renewal, or how often keys are updated (dynamic keys), with a security off-period. It introduces a framework for determining the best key refresh rate based on the anticipated rate at which keys might be compromised. Furthermore, the proposed framework is scalable, allowing new nodes to quickly join the existing network. The system was tested with multiple nodes equipped with IEEE 802.11 interfaces, which were set in monitor mode to capture frames at the link layer. Nodes map their on-time frames onto their Bloom filters. Nodes exchange these Bloom filters in a feedback mechanism. Nodes extract those frames from their .pcap files, which are present in all Bloom filters; these are common frames among all nodes. These frames are used to form a shared secret that is passed to HMAC Key Derivation Function by each node to acquire the final encryption key of the required length. The validation of this encryption key is performed using a simple challenge-response protocol; upon successful validation, encrypted communication begins. Otherwise, the key generation process is restarted.

Distributed Ledgers and Secure Multiparty Computation for Financial Reporting and Auditing

Distributed Ledgers and Secure Multiparty Computation for Financial Reporting and Auditing

PEBASI: A Privacy preserving, Efficient Biometric Authentication Scheme based on Irises

We introduce a novel privacy-preserving biometric authentication scheme based on irises that allows a user to enroll once at a trusted biometric certification authority (BCA) and authenticate to online service providers (SPs) multiple times without involving the BCA during the authentication. Our scheme preserves the user’s biometric privacy from the SPs and transactional privacy from the BCA, while providing security against a malicious user. During the enrollment, the BCA issues a signed token that encrypts the user’s biometrics. We introduce techniques enabling the SP and the user to perform secure computation of biometric matching between such encrypted biometrics and the user’s biometrics captured at the authentication time. We provide a prototype implementation, a performance evaluation, and a security analysis of the protocol.

SMPTC3: Secure Multi-Party Protocol Based Trusted Cross-Chain Contracts

We propose an innovative approach called SMPTC3, designed specifically to enhance security and privacy in cross-chain transaction verification. This approach addresses multi-chain, multi-participant information exchange and large-scale cross-chain transfers, resisting various types of malicious attacks. We leverage the verifiability of cross-chain transactions based on smart contracts and innovatively transform transaction information into confidential sets, organizing them into quadratic secret polynomials. By utilizing secret sharing and random distribution techniques, we construct a secure multiparty computation method, tailored for cross-chain transactions. To enhance the efficiency of cross-chain transactions, we introduce cross-chain batch processing technology, grouping inter-chain transactions into cross-chain transaction sets. Unlike traditional distributed notary technologies, SMPTC3 designates honest participants as a cross-chain notary group, reducing the time required for redundant signature confirmations and significantly lowering the possibility of malicious notaries. Theoretical analysis and empirical experiments demonstrate that SMPTC3 is highly efficient in addressing cross-chain transaction security issues.

Secure similar patients query with homomorphically evaluated thresholds

Patient-centric precision medicine requires the analysis of large volumes of genomic data to tailor treatments and medications based on individual-level characteristics. Because the amount of data held by a single institution is limited, researchers may want access to genomic data held by other institutions. Owing to the inherent privacy implications of genomic data, performing comparisons on encrypted data is preferable in certain settings. The Similar patient query (SPQ) is an application that enables a secure search across genomic databases for patients with similar genetic makeup. Query results can be used to draw meaningful conclusions regarding suitable therapies.However, existing protocols either reveal intermediate computations, such as similarity scores, which can lead to membership-inference attacks, or they realize the ideal Boolean output (similar/not similar) through multiple protocol rounds, requiring the database owners to stay online throughout.This paper introduces a two-party privacy-preserving approach to perform SPQs across encrypted genomic databases based on secure function extensions of additively homomorphic encryption. In contrast to related works, our scheme enables secure computation of genomic data similarity without an external party in a single round. This is achieved for more than 1000 positions of a genome in a single public key operation of 256-bit security level in the integer factorization setting.

LLM-Twin: mini-giant model-driven beyond 5G digital twin networking framework with semantic secure communication and computation

Beyond 5G networks provide solutions for next-generation communications, especially digital twins networks (DTNs) have gained increasing popularity for bridging physical and digital space. However, current DTNs pose some challenges, especially when applied to scenarios that require efficient and multimodal data processing. Firstly, current DTNs are limited in communication and computational efficiency, since they require to transmit large amounts of raw data collected from physical sensors, as well as to ensure model synchronization through high-frequency computation. Second, current models of DTNs are domain-specific (e.g. E-health), making it difficult to handle DT scenarios with multimodal data processing requirements. Finally, current security schemes for DTNs introduce additional overheads that impair the efficiency. Against the above challenges, we propose a large language model (LLM) empowered DTNs framework, LLM-Twin. First, based on LLM, we propose digital twin semantic networks (DTSNs), which enable more efficient communication and computation. Second, we design a mini-giant model collaboration scheme, which enables efficient deployment of LLM in DTNs and is adapted to handle multimodal data. Then, we designed a native security policy for LLM-twin without compromising efficiency. Numerical experiments and case studies demonstrate the feasibility of LLM-Twin. To our knowledge, this is the first to propose an LLM-based semantic-level DTNs.

A novel quantum security multi-party extremum protocol in a d-dimensional quantum system

Secure multi-party extremum computation (SMEC) is a specific application scenario of secure multi-party computation, which allows multiple participants to compute the extremum of data without disclosing private information. The extremum includes maximum, minimum, sum of extremums, and difference of extremums. SMEC has wide applications in financial transactions, market analysis, sports events, healthcare, etc. Current protocol research mainly exists in the classical domain and cannot withstand quantum computing attacks. To address this issue, we propose a novel QSME protocol based on a d-dimensional quantum system, capable of computing the maximum and minimum values among multi-party data under unconditional security, and can compute the sum and difference of extremums without disclosing the maximum and minimum values, to adapt to complex application scenarios. The article proposes a coding method for a d-dimensional quantum system to further enhance security, provides correctness analysis, security analysis, robustness analysis, and comparative analysis, and proposes an experimental method for a d-dimensional quantum system to verify the effectiveness of the protocol, demonstrating strong practicality.

Big Data and Privacy with a focus on Statistical Approaches to Ensuring Data Confidentiality

In the era of Big Data, where vast datasets fuel innovation across industries, the paramount concern remains safeguarding individual privacy. This article explores how statistical approaches ensure data confidentiality amidst the proliferation of digital information. Differential privacy techniques introduce calibrated noise to protect identities while preserving data utility, crucial for compliance and trust in data-driven decision-making. Secure Multiparty Computation (MPC) enables collaborative analysis without exposing raw data, supporting privacy in sectors like healthcare and finance. Privacy-preserving data mining techniques integrate encryption and anonymization to extract insights while shielding sensitive information. Anonymization and de-identification methods further bolster privacy by masking identifiable data, essential for adhering to stringent regulations like GDPR and HIPAA. As data generation escalates, advancing these statistical methods is essential for maintaining privacy integrity in the evolving landscape of Big Data applications. Keywords: Big Data, Privacy, Statistical approaches, Differential privacy, Secure multiparty computation.

Blockchain-assisted Verifiable Secure Multi-Party Data Computing

Secure multi-party computation (SMPC) is a crucial technology that supports privacy preservation, enabling multiple users to perform computations on any function without disclosing their private inputs and outputs in a distrustful environment. Existing secure multi-party computation models typically rely on obfuscation circuits and cryptographic protocols to facilitate collaborative computation of tasks. However, the efficiency and privacy leakage of users have not been paid much attention during the computation process. To address these problems, this article proposes a privacy-preserving approach Blockchain-assisted Verifiable Secure Multi-Party Data Computing (BVS-MPDC). Specifically, to prevent privacy leakage when users and multiple participants share data, BVS-MPDC uses additive homomorphic encryption to encrypt data shares; and verifies the generated Pedersen commitment of all the data. BVS-MPDC utilizes an improved Schnorr aggregation signature to improve computation efficiency between computing nodes and smart contracts by submitting an aggregation signature to the blockchain. Moreover, we design and implement a smart contract for verifying aggregation signature results on Ethereum. The security proof is presented under the UC framework. Finally, simulation experiments of performance evaluations demonstrate that our scheme outperforms existing schemes in computation overhead and verification.

SecretFlow-SCQL: A Secure Collaborative Query Platform

In the business scenarios at Ant Group, there is a rising demand for collaborative data analysis among multiple institutions, which can promote health insurance, financial services, risk control, and others. However, the increasing concern about privacy issues has led to data silos. Secure Multi-Party Computation (MPC) provides an effective solution for collaborative data analysis, which can utilize data value while ensuring data security. Nevertheless, the performance bottlenecks of MPC and the strong demand for scalability pose great challenges to secure collaborative data analysis frameworks. In this paper, we build a secure collaborative data analysis system SCQL with a general purpose. We design more efficient MPC protocols and relational operators to meet the demand for scalability. In terms of system design, we aim to implement a system with security, usability, and efficiency. We conduct extensive experiments on SCQL to validate our optimization improvements: (1) Our optimized secure sort protocol sorts one million 64-bit data in only 4.5 minutes, 126× faster than EMP (9.4 hours). (2) The end-to-end execution time of the typical vertical scenario query is reduced by 1991× from the state-of-the-art semi-honest collaborative analysis framework Secrecy (rewritten with Additive Secret Sharing protocol), with appropriate security tradeoffs. (3) We test the system in the WAN setting with input size = 10 7 to demonstrate the scalability. We have successfully deployed SCQL to address problems in real-world business scenarios at Ant Group.

FedSQ: A Secure System for Federated Vector Similarity Queries

Vector databases have emerged as crucial tools for managing and retrieving representation embeddings of unstructured data. Given the explosive growth of data, vector data is often distributed and stored across multiple organizations. However, privacy concerns and regulations like GDPR present new challenges in collaborative and secure queries, also known as federated queries, over those vector data distributed across various data owners. Although existing research has attempted to enable such query services for low-dimensional data, such as relational and spatial data, these solutions can be inefficient in answering vector similarity queries involving high-dimensional data. Therefore, we are motivated to develop a new prototype system called FedSQ that (1) ensures privacy protection across data owners and (2) balances query efficiency and result accuracy when processing federated vector similarity queries. To achieve these goals, FedSQ utilizes advanced secure multi-party computation techniques to prevent information leakage during query processing and incorporates indexing and sampling based optimizations to strike a proper performance balance.

Applications of Homomorphic Encryption in Secure Computation

Background Homomorphic encryption (HE) represents a pivotal innovation in modern cryptography, offering a pathway to secure computation on encrypted data. This paper embarks on a comprehensive exploration of HE's applications, elucidating its transformative potential in bolstering data security and privacy across various domains. Methods The research employs a mixed-methods approach to evaluate HE technologies. Quantitatively, it develops realistic datasets simulating healthcare and financial data, assessing HE's performance in encrypted computations. Various encryption schemes are rigorously tested for efficiency and accuracy under different conditions. Qualitatively, insights from expert interviews and case studies of HE implementations provide additional context on practical challenges and strategic benefits. Results The simulations and analyses showcase the efficiency, scalability, and security of HE techniques in diverse scenarios. The empirical evidence validates the real-world applicability of HE, demonstrating its versatility and efficacy in secure computation outsourcing, privacy-preserving data analysis, and secure multi-party computation. Conclusions This research paper highlights the transformative power of homomorphic encryption, advocating for its widespread adoption and integration. By bridging the gap between theoretical understanding and practical implementation, the paper contributes to advancing secure computation practices, addressing contemporary challenges in data security and privacy amidst evolving cybersecurity threats and the increasing ubiquity of sensitive data. In essence, this research serves as a beacon of insight into the future of data confidentiality and integrity, promoting HE as a crucial tool for revolutionizing the landscape of data security and privacy in an interconnected world.

SENTINEY: Securing ENcrypted mulTI-party computatIoN for Enhanced data privacY and phishing detection

Phishing attacks have recently become a real danger that threatens the security of sensitive data. This research paper presents a new approach based on Secure Multi-Party Computation (SMPC) to identify phishing attacks on encrypted emails while ensuring data confidentiality and privacy. The proposed approach, SENTINEY (Securing ENcrypted mulTIparty computatIoN for Enhanced data privacY and phishing detection), combines unsupervised machine learning and string matching techniques to detect phishing links over encrypted data, making it adaptive. Subsequently, an adaptive system dynamically selects the appropriate phishing detection technique taking into account various factors (e.g, the volume of new attacks, the accuracy of the machine learning model, attack specificity and available system resources) is suggested. For efficiency reasons, the learning model uses network virtualization features to improve computational resources. This new approach has shown good performance in taking advantage of network virtualization to create a secure and collaborative environment for the SMPC. The dataset used to train and test the proposal is generated on the basis of real phishing emails, including real phishing URLs and keywords. An in-depth performance analysis evaluated the performance of the proposed approach in terms of efficiency (processing time) and robustness (accuracy, precision, recall and F1 score). Simulation results and comparison with relevant solutions show that the proposed approach achieves superior robustness at lower costs. Using a string-matching approach, the multilayer perceptron achieved the highest accuracy of 98% with a detection time of 0.89 s. On the other hand, Isolation Forest showed high efficiency in combating zero-day phishing attacks. The MLP model combined with other tools achieved an accuracy of 99.4%.

Privacy Preservation of Large Language Models in the Metaverse Era: Research Frontiers, Categorical Comparisons, and Future Directions

ABSTRACTLarge language models (LLMs), with their billions to trillions of parameters, excel in natural language processing, machine translation, dialog systems, and text summarization. These capabilities are increasingly pivotal in the metaverse, where they can enhance virtual interactions and environments. However, their extensive use, particularly in the metaverse's immersive platforms, raises significant privacy concerns. This paper analyzes existing privacy issues in LLMs, vital for both traditional and metaverse applications, and examines protection techniques across the entire life cycle of these models, from training to user deployment. We delve into cryptography, embedding layer encoding, differential privacy and its variants, and adversarial networks, highlighting their relevance in the metaverse context. Specifically, we explore technologies like homomorphic encryption and secure multiparty computation, which are essential for metaverse security. Our discussion on Gaussian differential privacy, Renyi differential privacy, Edgeworth accounting, and the generation of adversarial samples and loss functions emphasizes their importance in the metaverse's dynamic and interactive environments. Lastly, the paper discusses the current research status and future challenges in the security of LLMs within and beyond the metaverse, emphasizing urgent problems and potential areas for exploration.

Privacy-preserving inference resistant to model extraction attacks

Privacy-Preserving Deep Learning (PPDL) has been successfully applied in the inference phase to preserve the privacy of input data. However, PPDL models are vulnerable to model extraction attacks, in which an adversary attempts to steal the trained model itself. In this paper, we propose a new defense method against model extraction attacks that is specifically designed for PPDL based on secure multi-party computations and homomorphic encryption. The proposed method confounds inference queries for out-of-distribution data by using a fake network with the target network while optimizing computational efficiency for PPDL environments. Furthermore, we introduce Wasserstein regularization to ensure that the fake network’s output distribution is indistinguishable from the target network, thwarting adversaries’ attempts to discern any discrepancies within the PPDL framework. The experimental results demonstrate that our defense method attains a good accuracy-security trade-off and is effective against a wide range of attacks, including adaptive attacks and transfer attacks. Our work contributes to the field of PPDL by providing an extended perspective to improve the algorithm’s security and reliability beyond privacy.