Secure Computation Research Articles

Quantum secure multi-party computational geometry based on multi-party summation and multiplication

Secure multi-party computational geometry is a branch of secure multi-party computation, which is applied in many important fields. But up to now, the research on how to solve this problem with quantum methods has just started. Therefore, we study the design of quantum secure multi-party computational geometry (QSMCG) protocols in this paper. As the foundation, we extend the two-party summation and multiplication protocols proposed by Sutradhar and Om to multi-party protocols. Then we propose three new QSMCG protocols. Our proposed quantum secure two-party distance computation protocol has improvement in efficiency and accuracy. And the proposed quantum secure multi-party polytope volume computation protocol as well as the quantum secure circle–circle intersection area computation protocol are designed for the first time. Moreover, these protocols are designed to use multi-third parties, which solves the problem of intermediate value leakage in computation. The correctness, security and performance of all these protocols are analyzed.

Intelligent Reflecting Surface Assisted Secure Computation of Wireless Powered MEC System

The integration of mobile edge computing (MEC) and wireless power transfer (WPT) can effectively improve the computing ability and energy sustainability of energy-constrained wireless devices in the Internet of Things (IoT) networks. Intelligent reflecting surface (IRS) has recently emerged as an effective technique to improve the performance of wireless systems by intelligently reconfiguring wireless environments. This paper studies the exploitation of IRS to improve the secure computation performance of WPT-MEC systems with a passive eavesdropper. A wireless access point (AP) first charge multiple users with the emitted energy signals, and then the users perform local computing and partial offloading to complete their computation tasks with the harvested energy in the presence of an eavesdropper, where the local computing can be executed during the whole process of WPT and offloading. Meanwhile, deploying IRS can improve the energy capture and secure offloading performance of the users. We maximize the secure computation task bits of users by jointly optimizing the AP energy transmit beamforming, the IRS phase shifts, the transmit power, users' offloading time, and the local computation frequency of users, which are tangled with each other. An iterative optimal algorithm is developed to solve this non-convex problem by combining Taylor expansion method, semidefinite relaxation (SDR) algorithm, the Lagrange duality theory and Karush-Kuhn-Tucker (KKT) conditions. The numerical results show that the proposed scheme can effectively increase the secure computation task bits compared with other benchmark schemes, especially for the maximum transmit power of AP, the improvement is above 45 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Delegated Private Matching For Compute

Private matching for compute (PMC) establishes a match between two datasets owned by mutually distrusted parties (C and P) and allows the parties to input more data for the matched records for arbitrary downstream secure computation without rerunning the private matching component. The state-of-the-art PMC protocols only support two parties and assume that both parties can participate in computationally intensive secure computation. We observe that such operational overhead limits the adoption of these protocols to solely powerful entities as small data owners or devices with minimal computing power will not be able to participate. We introduce two protocols to delegate PMC from party P to untrusted cloud servers, called delegates, allowing multiple smaller P parties to provide inputs containing identifiers and associated values. Our Delegated Private Matching for Compute protocols, called DPMC and DsPMC, establish a join between the datasets of party C and multiple delegators P based on multiple identifiers and compute secret shares of associated values for the identifiers that the parties have in common. We introduce a rerandomizable encrypted oblivious pseudorandom function (OPRF) primitive, called EO, which allows two parties to encrypt, mask, and shuffle their data. Note that EO may be of independent interest. Our DsPMC protocol limits the leakages of DPMC by combining our EO scheme and secure three-party shuffling. Finally, our implementation demonstrates the efficiency of our constructions by outperforming related works by approximately 10x for the total protocol execution and by at least 20x for the computation on the delegators.

Extending the Security of SPDZ with Fairness

SPDZ refers to a family of protocols for Secure Multi-Party Computation (MPC) that lie at the foundation of very popular software frameworks for MPC, such as SCALE-MAMBA and MP-SPDZ. SPDZ provides good efficiency while guaranteeing security even when all but one of the participants are corrupted. This seemingly optimal property comes at a price: the protocol only offers security with abort, meaning that even a single cheating participant can force the protocol to abort, leaving honest participants with no clue on what the correct output is, or who cheated. This is especially problematic since cheating participants are able to obtain the correct output of the computation, effectively `stealing' it. We propose a *hybrid secure* adaptation to SPDZ, which retains the existing security guarantees, but in case the number of cheating players is less than half of the total, we achieve *fairness*, meaning that either all players obtain the correct output of the computation, or no player does. The `less than half' threshold of corrupted players has been proven to be a tight bound to achieve fairness. Aside from the description of the protocol and its security proof, we also present a proof-of-concept implementation, and evaluate its practical performance, thereby demonstrating that our solution has negligible overhead compared to standard SPDZ in most application scenarios.

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.

Research on Zero knowledge with machine learning

This research paper explores the intersection of zero-knowledge proofs (ZKPs) and machine learning (ML), presenting a comprehensive overview of recent advancements, applications, and challenges in this fast growing area. The jointers of ZKPs and ML techniques shall go a meter further to fuse privacy, security, and integrity in a number of solutions, which include forming of groups for data sharing and safe machine learning. Through the investigation of the well-respected sites in that area and also the thorough description of formulas and their experimental outcome, this paper looks for the clarification of the current state of affairs and the possible future directions of ZKPs in the AI world. By inserting the verification mechanism of ZKPs into machine learning ecosystem, it allows devising novel solutions for the problems of privacy and confidentiality that have for long been not solved. With this approach, the concatenation of parties collectively performs the process of dealing with private inputs without revealing any of these data and this, in return, opens the possibilities of secure multi-party computation. Furthermore, ZKPs protect data sharing as it gives people the opportunity to construct confidential data and share them to model training without compromising any one’s private details. Being a part of the dynamic conversations, which focus on the game-changing capacity of transparent zero-knowledge proofs (ZKPs), this paper brings the role of ZKPs in preserving the confidentiality and integrity of artificial intelligence (AI) applications into the centre of attention. As scientists still fight to improve protocols and circumvent computational complications, ZKPs are likely to establishment as critical tools in the effort to increase ML systems in the digital sphere.

Differential Fault Attacks on Privacy Protocols Friendly Symmetric-Key Primitives: RAIN and HERA

As the practical applications of fully homomorphic encryption (FHE), secure multi-party computation (MPC) and zero-knowledge (ZK) proof continue to increase, so does the need to design and analyze new symmetric-key primitives that can adapt to these privacy-preserving protocols. These designs typically have low multiplicative complexity and depth with the parameter domain adapted to their application protocols, aiming to minimize the cost associated with the number of nonlinear operations or the multiplicative depth of their representation as circuits. In this paper, we propose two differential fault attacks against a one-way function RAIN used for Rainier (CCS 2022), a signature scheme based on the MPC-in-the-head approach and an FHE-friendly cipher HERA used for the RtF framework (Eurocrypt 2022), respectively. We show that our attacks can recover the keys for both ciphers by only injecting a fault into the internal state and requiring only one normal and one faulty ciphertext blocks. Thus, we can use only the practical complexity of 226.6/228.8/230.4 bit operations to break the full-round RAIN with 128/192/256-bit keys. For full-round HERA with 80/128-bit key, our attack is practical with complexity the complexity of 220 encryptions with about 216 memory.

Efficient secure multi-party computation for proof of custody in Ethereum sharding

Efficient secure multi-party computation for proof of custody in Ethereum sharding

Private outsourced model predictive control via secure multi-party computation

Protecting data privacy is essential in developing practical cloud-based control systems to benefit from remote and distributed computation while avoiding the risks of disclosing sensitive information to potentially untrusted parties. This paper proposes a privacy-preserving framework to outsource the computation of model predictive control law to several untrusted cloud-based servers. A secret sharing scheme is employed to maintain the privacy of the system’s data in all the stages of the control loop in a secure multi-party computation environment. In this regard, a privacy-preserving algorithm is derived to securely implement a projected gradient method to solve the underlying optimization problem, without revealing private data to external eavesdroppers and curious cloud-based servers. The proposed method is solely based on secret sharing, and all computations can be performed securely by cloud servers without the need to designate target nodes or engage the system’s actuator in computing the intermediate steps. Therefore, the proposed method is less computationally demanding than the existing results based on homomorphic encryption and is applicable to systems with limited computing resources. Information-theoretic privacy assessments based on mutual information measures are provided. The efficacy of the proposed method is investigated on the cruise control problem of a freight train system.

Blockchain-enabled authentication framework for Maritime Transportation System empowered by 6G-IoT

In recent years, Maritime Transportation Systems (MTS) have experienced significant advancements through the adoption of Internet of Things (IoT) technology. The introduction of 6G mobile networks has further expanded possibilities by offering enhanced communication services and additional functionalities such as processing, caching, sensing, and control for a wide array of IoT devices. Despite these technological advancements, the integration of IoT and 6G has introduced new risks and challenges in terms of safety and reliability. The involvement of various maritime stakeholders in scheduling and managing marine transportation exacerbates these challenges. Moreover, the MTS has encountered escalating security and privacy concerns with unauthorized data access and message tampering pose significant vulnerabilities in the 6G-IoT-enabled MTS environment. Therefore, a shared and controlled access mechanism that cannot be manipulated or tampered with by unauthorized parties is also an essential requirement in MTS. To address these issues, this paper presents an authentication framework leveraging blockchain technology, specifically designed for 6G-IoT-enabled MTS. The objective is to handle real-time data using decentralized peer-to-peer cloud servers with minimal latency, all while addressing security and privacy concerns specific to MTS. This integration also serves to mitigate various security threats. The protocol’s security and privacy are verified through rigorous evaluations, including the ROR model, Scyther tool, and informal security analysis. A simulation of the proposed protocol using MIRACL is conducted, offering a comprehensive assessment of its computational costs and security features when compared to existing protocols, demonstrating its superior security and efficiency.

Local Community Detection in Multiple Private Networks

Individuals are often involved in multiple online social networks. Considering that owners of these networks are unwilling to share their networks, some global algorithms combine information from multiple networks to detect all communities in multiple networks without sharing their edges. When data owners are only interested in the community containing a given node, it is unnecessary and computationally expensive for multiple networks to interact with each other to mine all communities. Moreover, data owners who are specifically looking for a community typically prefer to provide less data than the global algorithms require. Therefore, we propose the Local Collaborative Community Detection problem (LCCD). It exploits information from multiple networks to jointly detect the local community containing a given node without directly sharing edges between networks. To address the LCCD problem, we present a method developed from M method, called colM, to detect the local community in multiple networks. This method adopts secure multiparty computation protocols to protect each network’s private information. Our experiments were conducted on real-world and synthetic datasets. Experimental results show that colM method could effectively identify community structures and outperform comparison algorithms.

PIoT-blockchain-enabled security framework for strengthening IoT device identity and data access

The Internet of Things (IoT) connects billions of devices, but concerns about data security and device identification have grown. This study introduces Proof of IoT (PIoT), a novel consensus method using blockchain-based smart contracts to ensure secure data transfer and device authentication in IoT networks. PIoT employs NuCypher-based security computations for authentication and data updating, ensuring only authorized parties access blockchain-recorded information. A reward-based filtering mechanism identifies malicious nodes, bolstering consensus integrity. Simulations demonstrate PIoT's superior performance compared to conventional methods, showing a 74.7% and 96.8% improvement over LPBFT and DAC4SH Consensus, respectively. PIoT enhances blockchain security under IoT by 15% compared to DAC4SH. Beyond facilitating secure IoT device communication, this approach mitigates various IoT attacks, offering a comprehensive solution to emerging IoT security challenges. ABBREVIATION: DAC4SH, Data access control scheme for smart home; DDoS, Distributed denial of service; DoS, Denial of Service; DTLS, Datagram Transport Layer Security; IoT, Internet of Things; LPBFT, Lightweight Particle Byzantine fault tolerance; MAC, Message authentication code; MITM, Man in the Middle; PBFT, Practical Byzantine Fault Tolerance; PIoT, Proof of IoT; TLS, Transport Layer Security

Application analysis of data encryption technology

Data encryption technology is a key method to secure sensitive data. The privacy and confidentiality of data is protected by encrypting the data, i.e., transforming the original data into a form that cannot be understood without authorization. However, the security of traditional encryption algorithms is gradually challenged with the increase in computing power. Therefore, studying how to apply more advanced encryption techniques has become a current research hotspot. This study aims to analyze the application of data encryption techniques and explore the advantages and applicability of emerging encryption algorithms. This paper presents a variety of methods for preventing users' privacy breaches, with a primary focus on the principles of homomorphic encryption and how data can be accessed by users without decryption. Additionally, the working principles of secure multiparty computation are discussed, allowing multiple users to perform calculations on shared data while preserving data privacy. Furthermore, the paper explores data encryption techniques that employ specific algorithms to convert plaintext into ciphertext, ensuring both data consistency and privacy. Finally, a summary and future prospects are provided.

LPP-BPSI: A location privacy-preserving scheme using blockchain and Private Set Intersection in spatial crowdsourcing

Leakage of location information can lead to malicious attacks on workers in spatial crowdsourcing (SC) and loss of security. In response, a location privacy preserving scheme based on blockchain and Private Set Intersection were proposed, which reduced the computational overhead of workers by using a cloud service provider for outsourcing secure computation while ensuring their location privacy. First, three smart contracts based on blockchain were designed that could eliminate the control of traditional spatial crowdsourcing servers on the task allocation process. Second, differential privacy of the location method was proposed to construct the privacy location, and the cloud service provider used the privacy location to perform the location matching calculation. Third, an improved variant of the Private Set Intersection algorithm was constructed for the case of matching multiple workers for a single task in spatial crowdsourcing, which could efficiently perform location matching while protecting location privacy. To further reduce the computational overhead, Bloom filters were utilized to filter out a large number of free workers with zero location relevance before location matching. The experimental results showed that the scheme was robust and practical in the case of a large number of workers and concurrent task requests.

United We Stand: Accelerating Privacy-Preserving Neural Inference by Conjunctive Optimization with Interleaved Nexus

Privacy-preserving Machine Learning as a Service (MLaaS) enables the powerful cloud server to run its well-trained neural model upon the input from resource-limited client, with both of server's model parameters and client's input data protected. While computation efficiency is critical for the practical implementation of privacy-preserving MLaaS and it is inspiring to witness recent advances towards efficiency improvement, there still exists a significant performance gap to real-world applications. In general, state-of-the-art frameworks perform function-wise efficiency optimization based on specific cryptographic primitives. Although it is logical, such independent optimization for each function makes noticeable amount of expensive operations unremovable and misses the opportunity to further accelerate the performance by jointly considering privacy-preserving computation among adjacent functions. As such, we propose COIN: Conjunctive Optimization with Interleaved Nexus, which remodels mainstream computation for each function to conjunctive counterpart for composite function, with a series of united optimization strategies. Specifically, COIN jointly computes a pair of consecutive nonlinear-linear functions in the neural model by reconstructing the intermediates throughout the whole procedure, which not only eliminates the most expensive crypto operations without invoking extra encryption enabler, but also makes the online crypto complexity independent of filter size. Experimentally, COIN demonstrates 11.2x to 29.6x speedup over various function dimensions from modern networks, and 6.4x to 12x speedup on the total computation time when applied in networks with model input from small-scale CIFAR10 to large-scale ImageNet.

Secure and efficient federated learning via novel multi-party computation and compressed sensing

Federated learning (FL) enables the full utilization of decentralized training without raw data. However, various attacks still threaten the training process of FL. To address these concerns, differential privacy (DP) and secure multi-party computation (SMC) are applied, but these methods may result in low accuracy and heavy training load. Moreover, the high communication consumption of FL in resource-constrained devices is also a challenging problem. In this paper, we propose a novel SMC algorithm for the FL (FL-IPFE) to protect the local gradients. It does not require a trusted third party (TTP) and is more suitable for FL. Furthermore, we propose a secure and efficient FL algorithm (SEFL), which applies compressed sensing (CS) and all-or-nothing transform (AONT) to minimize the number of transmitted and encrypted model updates. Additionally, our FL-IPFE is used to encrypt the last element of the preprocessed parameters for guaranteeing the security of the entire local model updates. Meanwhile, the issue of participant dropouts is also taken into account. Theoretical analyses demonstrate that our proposed algorithms can aggregate model updates with high security. Finally, experimental evaluation reveals that our SEFL possesses higher efficiency compared to other state-of-the-art works, while providing comparable model accuracy and strong privacy guarantees.

Novel quantum solutions to privacy-preserving point-line relation determination

Secure multiparty computational geometry is an essential field of secure multiparty computation. It involves solving geometric problems without disclosing any private geometric information from each participating party. Privacy-preserving point-line relation determination, a pivotal branch of secure multiparty computational geometry, holds significant potential for applications in various domains, including business, engineering, and military. In this paper, leveraging the assistance of a third party, we introduce a quantum solution to privacy-preserving point-line relation determination. This approach incorporates quantum key distribution and one-time pad encryption. Under the condition that the third party does not conspire with any participant, our protocol ensures heightened security and enhanced privacy protection compared to classical methods. This superiority is attributed to the underlying principles of quantum mechanics and the utilization of one-time pad encryption. Furthermore, we extend our protocol to involve multiple third parties, employing the technology of quantum secret sharing. In scenarios where these third parties refrain from colluding with any single participant, no information is divulged to the other participant. Consequently, the protocol becomes more rational and secure when multiple third parties are involved.

Reversible data hiding in encrypted image with secure multi-party for telemedicine applications

Privacy protection of electronic patient information (EPI) is a crucial concern in the telemedicine applications, especially when it comes to communication between patients and doctors. In this paper, we propose a high-capacity reversible data hiding (RDH) algorithm combined with secure multi-party computation for telemedicine applications. The pixel values within a 3 × 3-size image block are divided into embedded pixels (EPs) and sampled pixels (SPs), secret data is embedded into the prediction errors of EPs and SPs in two stages. In the first stage, two edge servers predict EPs using the four SPs in the block to obtain the prediction error of EPs. The secret data is embedded into EPs through addition operation and histogram shift method. In the second stage, the prediction error of SPs is calculated as the difference between EPs and SPs, and the embedding of secret data follows the same method as used in the first stage. The experimental results demonstrate that the proposed algorithm achieves a higher embedding rate compared to classical and state-of-the-art algorithms, with an average embedding rate of 0.47bpp on the UCID dataset. Furthermore, the proposed algorithm exhibits robustness against existing attacks.

A trustworthy data collection scheme based on active spot-checking in UAV-Assisted WSNs

The UAV-WSN systems integrates the flexibility of UAVs with the communication and sensing capabilities of WSNs, making them important means of information monitoring, collection, and analysis in various fields that has received widespread attention. However, various malicious attacks pose threats to the security of UAV-WSN systems, resulting in serious vulnerabilities in trustworthy data collection. Therefore, in this article, we propose an Active Spot-Checking-based Trustworthy Data Collection (ASC-DC) scheme, which makes the following main contributions: (1) A novel UAV-assisted active and verifiable node trust evaluation framework is proposed. It includes an active trust acquisition mechanism based on spot-checked factors of nodes and a node trustworthy interaction mechanism based on Authoritative Information Tables (AIT). Through actively spot-check nodes using UAV, this framework can rapidly and accurately evaluate node trustworthiness while assisting sensor nodes in establishing trust relationships quickly. (2) A layered trust computation model is proposed, where the primary trust computation and node security supervision tasks are offloaded to more powerful UAV, thereby reducing information congestion and node energy consumption caused by trust evaluation in the WSN. (3) An improved Ant Colony Optimization (ACO) algorithm is proposed to efficiently acquire UAV’s spot-checking flight trajectories. It reduces the cost of spot-checking nodes using UAV and improves the efficiency of trust verification. Experimental results demonstrate that the proposed ASC-DC scheme can accurately evaluate node trustworthiness in an efficient way, ensuring reliable data collection in UAV-WSN systems.

Large-scale quantum key distribution network simulator

The wide range of supported services in modern telecommunication networks has increased the demand for highly secure means of communication. Common security frameworks based on the computational security model are expected to become insecure due to significant advances in quantum computing. Quantum key distribution (QKD), a new secret key agreement primitive, enables long-anticipated practical information-theoretical security (ITS). Over the past two decades, academic and industrial communities have devoted their time and resources to developing QKD-based networks that can distribute and serve ITS keys to remote parties. However, because the availability of QKD network testbeds to the larger research community is limited and the deployment of such systems is costly and difficult, progress in this area is noticeably slow. To address this problem and spur future development and education, we provide a valuable, unique tool for simulating a QKD network. The tool is essential to testing novel network management methodologies applied to large-scale QKD networks. The simulator model contained in the tool was validated by simulating a network with six nodes and three pairs of users. The results indicate that the designed functional elements operate correctly.