Secure Multi-party Computing Research Articles

Secure Multi-Party Quantum Summation Based on Quantum Homomorphic Encryption

Secure multi-party computation has been playing a fundamental role in terms of classical cryptography. Quantum homomorphic encryption (QHE) could compute the encrypted data without decryption. At present, most protocols use a semi-honest third party (TP) to protect participants’ secrets. We use a quantum homomorphic encryption scheme instead of TP to protect the privacy of parties. Based on quantum homomorphic encryption, a secure multi-party quantum summation scheme is proposed in which N participants can delegate a server with strong quantum computing power to assist computation. By delegating the computation and key update processes to a server and a semi-honest key center, participants encrypt their private information data using Pauli operators to get the sum. Besides, the server can design and optimize the summation lines itself, and the correct results can be obtained even if the secret information is negative. The correctness analysis showed that the participants could correctly obtain the results of the calculation. The security analysis proves the scheme is resistant to both outside attack and participant’s attack, and is secure against collusive attack by up to N-2 participants. From the theoretical point of view, our protocol can extend to other secure multi-party computing problems.

Practical multi-party private collaborative k-means clustering

k-means clustering is widely used in many fields such as data mining, machine learning, and information retrieval. In many cases, users need to cooperate to perform k-means clustering tasks. How to perform clustering without revealing privacy has become a hot research topic. However, the existing k-means scheme based on secure multi-party computation cannot effectively protect the privacy of the output results. The multi-party k-means scheme based on differential privacy may lead to loss of data availability. In this article, we propose a practical protocol for k-means clustering in a collaborative manner, while protecting the privacy of each data record. Our protocol is the first to combine secure multi-party computing and differential privacy technology to train a privacy-preserving k-means clustering model. We design a novel algorithm, which is suitable for multi-party collaboration to update cluster centers without leaking data privacy. The algorithm guarantees that noise is added only once in each iteration, regardless of the number of participants. The protocol achieve the ”best of both worlds”, which can simultaneously achieves both the input privacy and the output privacy in the k-means clustering scheme. Evaluation of real data sets shows that our scheme has comparable running time compared with the k-means clustering scheme without privacy protection.

Efficient multi‐key fully homomorphic encryption over prime cyclotomic rings with fewer relinearisations

Multi-key fully homomorphic encryption (MKFHE) allows computations on ciphertexts encrypted by different users, which can be applied to implement secure multi-party computing (MPC). The current NTRU-based MKFHE has the following two drawbacks: One is that the relinearisation process during homomorphic evaluation is so complicated that the corresponding computation time is costly. The other is that a class of subfield attacks are proposed and affects the security of NTRU schemes over power-of-2 cyclotomic rings for large moduli q, especially for the NTRU-based fully homomorphic encryption (FHE) schemes. In this work, an efficient MKFHE scheme is proposed over prime cyclotomic rings with fewer relinearisations, which seems a good choice because of its potential to resist a subfield attack. More specifically, the time of the relinearisation process is reduced by half in homomorphic evaluations by separating the homomorphic multiplication and the relinearisation process (implementing two homomorphic multiplication operations together before relinearisation), while in current NTRU-type MKFHE schemes, these two processes are usually performed together. The error bound of the basic function components is re-analysed over prime cyclotomic rings in the average case, which can be used in the error analysis of our scheme. We construct an efficient NTRU-based single-key FHE scheme and an efficient MKFHE scheme over prime cyclotomic rings through relinearisation and modulus-switching techniques. The MKFHE scheme proposed has the on-the-fly property and has a tight ciphertext size compared with the GSW-type and BGV-type MKFHE schemes. An experiment shows that the homomorphic evaluation of the optimised single-key FHE scheme proposed is 1.9 times faster than an efficient NTRU-type MKFHE DHS16 proposed at DCC 2016.

Deep Learning Data Privacy Protection Based on Homomorphic Encryption in AIoT

With the rapid development of 5G technology, its high bandwidth, high reliability, low delay, and large connection characteristics have opened up a broader application field of IoT. Moreover, AIoT (Artificial Intelligence Internet of Things) has become the new development direction of IoT. Through deep learning of real-time data provided by the Internet of Things, AI can judge user habits more accurately, make devices behave in line with user expectations, and become more intelligent, thus improving product user experience. However, in the process, there is a lot of data interaction between the edge and the cloud. Given that the shared data contain a large amount of private information, preserving information security on the shared data is an important issue that cannot be neglected. In this paper, we combine deep learning with homomorphic encryption algorithm and design a deep learning network model based on secure multiparty computing (MPC). In the whole process, we realize that the cloud only owns the encryption samples of users, and users do not own any parameters or structural information related to the model. In the experimental part, we input the encrypted Mnist and Cifar-10 datasets into the model for testing, and the results show that the classification accuracy rate of the encrypted Mnist can reach 99.21%, which is very close to the result under plaintext. The classification accuracy rate of encrypted Cifar-10 can reach 91.35%, slightly lower than the test result in plaintext and better than the existing deep learning network model that can realize data privacy protection.

Privacy-Preserving Federated Learning Framework Based on Chained Secure Multiparty Computing

Federated learning (FL) is a promising new technology in the field of IoT intelligence. However, exchanging model-related data in FL may leak the sensitive information of participants. To address this problem, we propose a novel privacy-preserving FL framework based on an innovative chained secure multiparty computing technique, named chain-PPFL. Our scheme mainly leverages two mechanisms: 1) single-masking mechanism that protects information exchanged between participants and 2) chained-communication mechanism that enables masked information to be transferred between participants with a serial chain frame. We conduct extensive simulation-based experiments using two public data sets (MNIST and CIFAR-100) by comparing both training accuracy and leak defence with other state-of-the-art schemes. We set two data sample distributions (IID and NonIID) and three training models (CNN, MLP, and L-BFGS) in our experiments. The experimental results demonstrate that the chain-PPFL scheme can achieve practical privacy preservation (equivalent to differential privacy with $\epsilon $ approaching zero) for FL with some cost of communication and without impairing the accuracy and convergence speed of the training model.

Key technologies of Secure Multi-Party Computing for Perceived Data Transmission in Internet of Things

With the "wisdom of the Earth, connected to physical objects" makes it independent from the Internet gradually connected to the network and the Internet of Things. Internet of Things is another industrial upgrading of information industry after computer and mobile communication. The Internet of Things is based on the Internet. It uses technologies such as RFID, sensors and wireless sensor hardware to build a network information system covering all people and things in the world. It enables human economic, social life, production and operation and personal activities to operate in the intelligent Internet of Things. As an important support of national strategic emerging industries, the Internet of Things is the core and foundation of smart cities, national defense and intelligent manufacturing industries, and multi-domain, open and security are the theoretical requirements of the application of the Internet of Things. Aiming at the resource fragmentation problems caused by the closure of traditional the Internet of Things architecture, such as closure, tight coupling, poor scalability, and some security problems for the open sharing of the Internet of Things resources, this paper proposes an improved multi-domain Web of Things (MWoT) architecture based on the relevant standards of the Web of Things (WoT) architecture. Some related technologies, such as Internet of Things identification and edge cloud computing, are used to realize cross-domain sharing of Internet of Things resources and cross-domain collaboration of services, as well as to solve the problem of data transmission security.

Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review.

BackgroundThe novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.ObjectiveOur aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented.MethodsWe searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions.ResultsWe identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases.ConclusionsWe see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).

Privacy-preserving two-parties logistic regression on vertically partitioned data using asynchronous gradient sharing

The full application of machine learning has caused plenty of problems with privacy-preserving. Especially in multi-party machine learning, private data is often exposed in the aggregation,transmission, and communication phase, which leads to the problem of private data leakage. Existing works use secure multi-party computing (SMPC) or secret-sharing technology to ensure the privacy-preserving of multi-party machine learning. Nevertheless, it brings enormous cost and feasibility drawbacks. The partition method of datasets is one of the most critical factors affecting the performance of machine learning. Vertically partitioned data has the problems of incomplete feature information held by a single participant and complicated training process. Therefore, it has to be tackled urgently that how to efficiently and safely complete the multi-party training using vertically partitioned datasets. Moreover, training logistic regression models efficiently is one of the directions worth working on. In this paper, we propose a protocol using that can complete the logistic regression modeling of vertically partitioned data by asynchronous gradient sharing. At the same time, we use an efficient homomorphic encryption method to protect private data. The experiments show that our protocol can reduce the training time in the case of a small impact on the output results, and speedup can be over 10x. Meanwhile, it will ensure the security of the vertically partitioned dataset.

Lightweight Data Security Protection Method for AMI in Power Internet of Things

Aiming at the security problems caused by the access of a large number of new advanced metering system (AMI) equipment and the rapid growth of new business data interaction volume and interaction frequency, a lightweight data security protection method for power Internet of things (IoT) is proposed. Firstly, based on the “cloud-edge-end” AMI system architecture, a multilevel anonymous authentication method is proposed to reduce the complexity of low-end equipment access without reauthentication when smart meters and other devices access the system. Then, when fully homomorphic encryption is used for data encryption transmission, the lightweight packet recombination protocol is introduced, the lightweight hash function is used to reduce the calculation cost, and the sliding address window mechanism is used to reduce the packet loss rate. Finally, improved secure multiparty computing (SMPC) is used to achieve frequency hopping data aggregation, using shared key to calculate local shared value for key update, reducing data interaction between massive devices and AMI cloud security server, and improving broadband utilization in data aggregation process. The experiment results indicate that the proposed method obtained better utilization in bandwidth and shorter average data collection completion time. Besides, the proposed method can ensure the information security in the interaction process.

Social network analysis of law information privacy protection of cybersecurity based on rough set theory

PurposeThe purpose of this paper is to solve the problem of information privacy and security of social users. Mobile internet and social network are more and more deeply integrated into people’s daily life, especially under the interaction of the fierce development momentum of the Internet of Things and diversified personalized services, more and more private information of social users is exposed to the network environment actively or unintentionally. In addition, a large amount of social network data not only brings more benefits to network application providers, but also provides motivation for malicious attackers. Therefore, under the social network environment, the research on the privacy protection of user information has great theoretical and practical significance.Design/methodology/approachIn this study, based on the social network analysis, combined with the attribute reduction idea of rough set theory, the generalized reduction concept based on multi-level rough set from the perspectives of positive region, information entropy and knowledge granularity of rough set theory were proposed. Furthermore, it was traversed on the basis of the hierarchical compatible granularity space of the original information system and the corresponding attribute values are coarsened. The selected test data sets were tested, and the experimental results were analyzed.FindingsThe results showed that the algorithm can guarantee the anonymity requirement of data publishing and improve the effect of classification modeling on anonymous data in social network environment.Research limitations/implicationsIn the test and verification of privacy protection algorithm and privacy protection scheme, the efficiency of algorithm and scheme needs to be tested on a larger data scale. However, the data in this study are not enough. In the following research, more data will be used for testing and verification.Practical implicationsIn the context of social network, the hierarchical structure of data is introduced into rough set theory as domain knowledge by referring to human granulation cognitive mechanism, and rough set modeling for complex hierarchical data is studied for hierarchical data of decision table. The theoretical research results are applied to hierarchical decision rule mining and k-anonymous privacy protection data mining research, which enriches the connotation of rough set theory and has important theoretical and practical significance for further promoting the application of this theory. In addition, combined the theory of secure multi-party computing and the theory of attribute reduction in rough set, a privacy protection feature selection algorithm for multi-source decision table is proposed, which solves the privacy protection problem of feature selection in distributed environment. It provides a set of effective rough set feature selection method for privacy protection classification mining in distributed environment, which has practical application value for promoting the development of privacy protection data mining.Originality/valueIn this study, the proposed algorithm and scheme can effectively protect the privacy of social network data, ensure the availability of social network graph structure and realize the need of both protection and sharing of user attributes and relational data.

CERTIFICATE REVOCATION SCHEME BASED ON WEIGHTED VOTING GAME AND RATIONAL SECURE MULTIPARTY COMPUTING

The Mobile Adhoc Network consists of deployed mobile nodes which lead to the frequent changes in network topology. Due to topology changes, required infrastructure is unavailable for communication. Moreover, malicious nodes present in MANET make use of this modification and can easily launch highly vulnerable attacks on the routing path of the network. Hence, Security issue such as removing misbehaving nodes is the primary issue in MANET. Effective certificate revocation scheme was introduced to identify and eliminate the node with malicious activities in the network based on the weighted voting game (ECR-WVG) approach. In this approach, weights and quota were two factors, determined for an effective revocation of malicious nodes certificates. However, security during multiparty transmission was not taken into account in ECR-WVG. In Effective Certificate Revocation Scheme based on Weighted Voting Game and Rational Secure Multi-Party Computing (ECR-WVG-RSMPC) method, rational secret sharing scheme is introduced along with ECR-WVG approach for securing multiparty transmission. Performance evaluation can be done between ECR-WVG and ECR-WVG-RSMPC in terms of false revocation, malicious node revocation, normalized time for revocation and revocation accuracy ratio.

Healthcare Data Gateways: Found Healthcare Intelligence on Blockchain with Novel Privacy Risk Control.

Healthcare data are a valuable source of healthcare intelligence. Sharing of healthcare data is one essential step to make healthcare system smarter and improve the quality of healthcare service. Healthcare data, one personal asset of patient, should be owned and controlled by patient, instead of being scattered in different healthcare systems, which prevents data sharing and puts patient privacy at risks. Blockchain is demonstrated in the financial field that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we proposed an App (called Healthcare Data Gateway (HGD)) architecture based on blockchain to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private. Our proposed purpose-centric access model ensures patient own and control their healthcare data; simple unified Indicator-Centric Schema (ICS) makes it possible to organize all kinds of personal healthcare data practically and easily. We also point out that MPC (Secure Multi-Party Computing) is one promising solution to enable untrusted third-party to conduct computation over patient data without violating privacy.

Rational computing protocol based on fuzzy theory

Secure multi-party computing (SMPC) is often used to solve security problems in cloud computing. Rational SMPC is a kind of SMPC in the presence of rational parties, who wish to maximize their utilities. Previous works about rational SMPC only studied the security properties under complete information scenario, where parties' types are common knowledge. However, parties in practical applications have private types, which is unknown to others. This scenario is called incomplete information. In this paper, rational parties are allowed to have private types, which affect their utilities. Previously, rational parties obtain expected utilities due to unknown private types under incomplete information scenario. However, rational parties prefer to obtain pure utilities in actual life. To solve this contradiction, we use fuzzy theory to confirm the private type of his opponent; then they execute the protocol as if they know the private types just like the execution under complete information scenario. Consequently, they obtain pure utilities other than expected utility. In addition, our protocol can reduce round complexity than previous ones. Consequently, it will improve the security level and efficiency of cloud computing.

A brief survey on secure multi-party computing in the presence of rational parties

Intelligent agents (IA) are autonomous entities which observe through sensors and act upon an environment using actuators to adjust their activities towards achieving certain goals. The architectures of agents have enormous potentials when they are applied into critical systems, where agents choose actions between working with their own and cooperating with others. Rational utility-based agents choose actions to maximize their expected utilities. Rational secure multi-party computing (RSMPC) means secure multi-party computing (SMPC) in the presence of rational utility-based agents. Here, we call them rational parties. In this paper certain goals of rational parties are maximizing their utilities. The introduction of rational parties considers the incentives in executing protocols. The security definitions under rational framework can better demonstrate the executing environment of real protocols. Furthermore, rational two-party computing removes some impossibility in traditional two-party computing, such as fairness. This paper represents the research status of RSMPC and some typical protocols. The advantages and disadvantages of previous rational SMPC protocols are discussed here. As an emerging field, there are still lots of open problems in RSMPC, such as communication channels, utility assumptions and equilibrium notions etc.

A flexible approach to distributed data anonymization

Sensitive biomedical data is often collected from distributed sources, involving different information systems and different organizational units. Local autonomy and legal reasons lead to the need of privacy preserving integration concepts. In this article, we focus on anonymization, which plays an important role for the re-use of clinical data and for the sharing of research data. We present a flexible solution for anonymizing distributed data in the semi-honest model. Prior to the anonymization procedure, an encrypted global view of the dataset is constructed by means of a secure multi-party computing (SMC) protocol. This global representation can then be anonymized. Our approach is not limited to specific anonymization algorithms but provides pre- and postprocessing for a broad spectrum of algorithms and many privacy criteria. We present an extensive analytical and experimental evaluation and discuss which types of methods and criteria are supported. Our prototype demonstrates the approach by implementing k-anonymity, ℓ-diversity, t-closeness and δ-presence with a globally optimal de-identification method in horizontally and vertically distributed setups. The experiments show that our method provides highly competitive performance and offers a practical and flexible solution for anonymizing distributed biomedical datasets.

Behavioral Identification of Trusted Third Party in Secure Multiparty Computing Protocol

We present a solution for identification and reduction of malicious conduct by Trusted Third parties (TTPs) in Secure Multiparty Computing Protocol. This paper also proposes a secured protocol for computation and defines encryption to be performed before sending inputs for computation. Our protocol uses eenvelopes for sharing keys between parties and TTPs. This key sharing is done on the basis of RSA algorithm. This ensures that parties send their data in encrypted manner to TTPs in order to maintain privacy and security of inputs. Also, single and multi trusted third party model is compared and the probabilistic evidences for them have been analyzed with security analysis graphs.