Secure Computation Efficiency Resource Allocation for Massive MIMO-Enabled Mobile Edge Computing Networks

In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)network.We first derive the secure transmission rate based on the mMIMO under imperfect channel state information.Based on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit power.Due to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach method.Next,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the solutions.Finally,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes.

EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.

Secure and Efficient Outsourced Computation in Cloud Computing Environments

Secure and efficient outsourced computation in cloud computing environments is crucial for ensuring data confidentiality, integrity, and resource optimization. In this research, we propose novel algorithms and methodologies to address these challenges. Through a series of experiments, we evaluate the performance, security, and efficiency of the proposed algorithms in real-world cloud environments. Our results demonstrate the effectiveness of homomorphic encryption-based secure computation, secure multiparty computation, and trusted execution environment-based approaches in mitigating security threats while ensuring efficient resource utilization. Specifically, our homomorphic encryption-based algorithm exhibits encryption times ranging from 20 to 1000 milliseconds and decryption times ranging from 25 to 1250 milliseconds for payload sizes varying from 100 KB to 5000 KB. Furthermore, our comparative analysis against state-of-the-art solutions reveals the strengths of our proposed algorithms in terms of security guarantees, encryption overhead, and communication latency.

A Secure Multiparty Quantum Homomorphic Encryption Scheme

The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private data.In this paper,a novel secure multiparty quantum homomorphic encryption scheme is proposed,which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest server.Firstly,each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and key.Secondly,with the help of the almost dishonest server,the non-maximally entangled states are preshared between the client and the server to correct errors in the homomorphic evaluation of T gates,so as to realize universal quantum circuit evaluation on encrypted data.Thirdly,from the perspective of the application scenario of secure multi-party computation,this work is based on the probabilistic quantum homomorphic encryption scheme,allowing multiple parties to delegate the server to perform the secure homomorphic evaluation.The operation and the permission to access the data performed by the client and the server are clearly pointed out.Finally,a concrete security analysis shows that the proposed multiparty quantum homomorphic encryption scheme can securely resist outside and inside attacks.

Secure planar convex hull protocol for large-scaled point sets in semi-honest model

Efficiency and scalability are still the bottleneck for secure multi-party computation geometry （SMCG）. In this work a secure planar convex hull （SPCH） protocol for large-scaled point sets in semi-honest model has been proposed efficiently to solve the above problems. Firstly, a novel priva- cy-preserving point-inclusion （PPPI） protocol is designed based on the classic homomorphic encryp- tion and secure cross product protocol, and it is demonstrated that the complexity of PPPI protocol is independent of the vertex size of the input convex hull. And then on the basis of the novel PPPI pro- tocol, an effective SPCH protocol is presented. Analysis shows that this SPCH protocol has a good performance for large-scaled point sets compared with previous solutions. Moreover, analysis finds that the complexity of our SPCH protocol relies on the size of the points on the outermost layer of the input point sets only.

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.

TWO PRIVACY-PRESERVING PROTOCOLS FOR POINT-CURVE RELATION

Numerous privacy-preserving issues have emerged along with the fast development of Internet, both in theory and in real-life applications. To settle the privacy-preserving problems, secure multi-party computation is essential and critical. In this paper, we have solved two problems regarding to how to determine the position relation between points and curves without revealing any private information. Two protocols have been proposed in order to solve the problems in different conditions. In addition, some building blocks have been developed, such as scalar product protocol, so that we can take advantage of them to settle the privacy-preserving computational geometry problems which are a kind of special secure multi-party computation problems. Moreover, oblivious transfer and power series expansion serve as significant parts in our protocols. Analyses and proofs have also been given to argue our conclusion.

Robust Threshold Guillou-Quisquater Signature Scheme

The deficiencies of the first threshold Guilbu-Quisquater signature schemepresented by Li-San Liu, Cheng-Kang Chu and Wen-Guey Tzeng arc analysiscd at first, and then a newthreshold Guillou-Quisquater signature scheme is presented. The new scheme isunforgeable and robustagainst any adaptive adversary if the base Guillou-Quisquater signature scheme is unforgeable underthe chosen message attack and computing the discrete logarithm modulo a prime is hard This schemecan also achieve optimal resilience. However, the new scheme does not need the assumption that N isthe product of two safe primes. The basie signature scheme underlying the new scheme is exactlyGuillou-Quisqualtr signature scheme, and the additional strong computation assumption introduced bythe first threshold Guillou-Quisquater scheme is weaken.

A NEW PRIVACY-PRESERVING EUCLID-DISTANCE PROTOCOL AND ITS APPLICATIONS IN WSNS

Recently, privacy concerns become an increasingly critical issue. Secure multi-party computation plays an important role in privacy-preserving. Secure multi-party computational geometry is a new field of secure multi-party computation. In this paper, we devote to investigating the solutions to some secure geometric problems in a cooperative environment. The problem is collaboratively computing the Euclid-distance between two private vectors without disclosing the private input to each other. A general privacy-preserving Euclid-distance protocol is firstly presented as a building block and is proved to be secure and efficient in the comparison with the previous methods. And we proposed a new protocol for the application in Wireless Sensor Networks (WSNs), based on the novel Euclid-distance protocol and Density-Based Clustering Protocol (DBCP), so that the nodes from two sides can compute cooperatively to divide them into clusters without disclosing their location information to the opposite side.

Research on Privacy Preserving Data Mining

In recent years,with the explosive development in Internet,data storage and data processing technologies,privacy preservation has been one of the greater concerns in data mining.A number of methods and techniques have been developed for privacy preserving data mining.This paper provided a wide survey of different privacy preserving data mining algorithms and analyzed the representative techniques for privacy preservation.The existing problems and directions for future research are also discussed.

Robust peer-to-peer learning via secure multi-party computation

To solve the data island problem,federated learning(FL)provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation.Peer-to-peer(P2P)federated learning further improves the robustness of the system,in which there is no server and each client communicates directly with the other.For secure aggregation,secure multi-party computing(SMPC)protocols have been utilized in peer-to-peer manner.However,the ideal SMPC protocols could fail when some clients drop out.In this paper,we propose a robust peer-to-peer learning(RP2PL)algorithm via SMPC to resist clients dropping out.We improve the segmentbased SMPC protocol by adding a check and designing the generation method of random segments.In RP2PL,each client aggregates their models by the improved robust secure multi-part computation protocol when finishes the local training.Experimental results demonstrate that the RP2PL paradigm can mitigate clients dropping out with no significant degradation in performance.

Verifiable Privacy-Preserving Neural Network on Encrypted Data

The widespread acceptance of machine learning,particularly of neural networks leads to great success in many areas,such as recommender systems,medical predictions,and recognition.It is becoming possible for any individual with a personal electronic device and Internet access to complete complex machine learning tasks using cloud servers.However,it must be taken into consideration that the data from clients may be exposed to cloud servers.Recent work to preserve data confidentiality has allowed for the outsourcing of services using homomorphic encryption schemes.But these architectures are based on honest but curious cloud servers,which are unable to tell whether cloud servers have completed the computation delegated to the cloud server.This paper proposes a verifiable neural network framework which focuses on solving the problem of data confidentiality and training integrity in machine learning.Specifically,we first leverage homomorphic encryption and extended diagonal packing method to realize a privacy-preserving neural network model efficiently,it enables the user training over encrypted data,thereby protecting the user’s private data.Then,considering the problem that malicious cloud servers are likely to return a wrong result for saving cost,we also integrate a training validation modular Proof-of-Learning,a strategy for verifying the correctness of computations performed during training.Moreover,we introduce practical byzantine fault tolerance to complete the verification progress without a verifiable center.Finally,we conduct a series of experiments to evaluate the performance of the proposed framework,the results show that our construction supports the verifiable training of PPNN based on HE without introducing much computational cost.

Non-interactive SM2 threshold signature scheme with identifiable abort

A threshold signature is a special digital signature in which the N-signer share the private key x and can construct a valid signature for any subset of the included t-signer,but less than t-signer cannot obtain any information.Considering the breakthrough achievements of threshold ECDSA signature and threshold Schnorr signature,the existing threshold SM2 signature is still limited to two parties or based on the honest majority setting,there is no more effective solution for the multiparty case.To make the SM2 signature have more flexible application scenarios,promote the application of the SM2 signature scheme in the blockchain system and secure cryptocurrency wallets.This paper designs a non-interactive threshold SM2signature schemebasedon partially homomorphic encryption and zero-knowledge proof.Only the last round requires the message input,so make our scheme non-interactive,and the pre-signing process takes 2 rounds of communication to complete after the key generation.We allow arbitrary threshold t<n and design a key update strategy.It can achieve security with identifiable abort under the malicious majority,which means that if the signature process fails,we can find the failed party.Performance analysis shows that the computation and communication costs of the pre-signing process grows linearly with the parties,and it is only 1/3 of the Canetti's threshold ECDSA(CCS'20).

Secure Two-Party Computational Geometry

Secure Multi-party Computation has been a research focus in international cryptographic community in recent years. In this paper the authors investigate how some computational geometric problems could be solved in a cooperative environment, where two parties need to solve a geometric problem based on their joint data, but neither wants to disclose its private data to the other party. These problems are the distance between two private points, the relation between a private point and a circle area, the relation between a private point and an ellipse area and the shortest distance between two point sets. The paper gives solutions to these specific geometric. problems, and in doing so a building block is developed, the protocol for the distance between two private points, that is also useful in the solutions to other geometric problems and combinatorial problems.

Secure Two-Party Point-Circle Inclusion Problem

Privacy-preserving computational geometry is a special secure multi-party computation and has many applications. Previous protocols for determining whether a point is inside a circle are not secure enough. We present a two-round protocol for computing the distance between two private points and develop a more efficient protocol for the point-circle inclusion problem based on the distance protocol. In comparison with previous solutions, our protocol not only is more secure but also reduces the number of communication rounds and the number of modular multiplications significantly.

Secure Two-Party Distance Computation Protocol Based on Privacy Homomorphism and Scalar Product in Wireless Sensor Networks

Numerous privacy-preserving issues have emerged along with the fast development of the Internet of Things. In addressing privacy protection problems in Wireless Sensor Networks （WSN）, secure multi-party computation is considered vital, where obtaining the Euclidian distance between two nodes with no disclosure of either side＇s secrets has become the focus of location-privacy-related applications. This paper proposes a novel Privacy-Preserving Scalar Product Protocol （PPSPP） for wireless sensor networks. Based on PPSPP, we then propose a Homomorphic-Encryption-based Euclidean Distance Protocol （HEEDP） without third parties. This protocol can achieve secure distance computation between two sensor nodes. Correctness proofs of PPSPP and HEEDP are provided, followed by security validation and analysis. Performance evaluations via comparisons among similar protocols demonstrate that HEEDP is superior; it is most efficient in terms of both communication and computation on a wide range of data types, especially in wireless sensor networks.

A Secure Scalar Product Protocol Against Malicious Adversaries

A secure scalar product protocol is a type of specific secure multi-party computation problem. Using this kind of protocol, two involved parties are able to jointly compute the scalar product of their private vectors：, but no party will reveal any information about his/her private vector to another one. The secure scalar product protocol is of great importance in many privacy-preserving applications such as privacy-preserving data mining, privacy-preserving cooperative statistical analysis, and privacy-preserving geometry computation. In this paper, we give an efficient and secure scalar product protocol in the presence of malicious adversaries based on two important tools： the proof of knowledge of a discrete logarithm and the verifiable encryption. The security of the new protocol is proved under the standard simulation-based definitions. Compared with the existing schemes, our scheme offers higher efficiency because of avoiding inefficient cut-and-choose proofs.

Concretely ecient secure multi-party computation protocols:survey and mor

Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the function.In the last decade,MPC has rapidly moved from a purely theoretical study to an object of practical interest,with a growing interest in practical applications such as privacy-preserving machine learning(PPML).In this paper,we comprehensively survey existing work on concretely ecient MPC protocols with both semi-honest and malicious security,in both dishonest-majority and honest-majority settings.We focus on considering the notion of security with abort,meaning that corrupted parties could prevent honest parties from receiving output after they receive output.We present high-level ideas of the basic and key approaches for designing di erent styles of MPC protocols and the crucial building blocks of MPC.For MPC applications,we compare the known PPML protocols built on MPC,and describe the eciency of private inference and training for the state-of-the-art PPML protocols.Further-more,we summarize several challenges and open problems to break though the eciency of MPC protocols as well as some interesting future work that is worth being addressed.This survey aims to provide the recent development and key approaches of MPC to researchers,who are interested in knowing,improving,and applying concretely ecient MPC protocols.

Nearly universal and efficient quantum secure multi-party computation protocol

Universality is an important property in software and hardware design.This paper concentrates on the universality of quantum secure multi-party computation(SMC)protocol.First of all,an in-depth study of universality has been conducted,and then a nearly universal protocol is proposed by using the Greenberger-Horne-Zeilinger(GHZ)-like state and stabilizer formalism.The protocol can resolve the quantum SMC problem which can be deduced as modulo subtraction,and the steps are simple and effective.Secondly,three quantum SMC protocols based on the proposed universal protocol:Quantum private comparison(QPC)protocol,quantum millionaire(QM)protocol,and quantum multi-party summation(QMS)protocol are presented.These protocols are given as examples to explain universality.Thirdly,analyses of the example protocols are shown.Concretely,the correctness,fairness,and efficiency are confirmed.And the proposed universal protocol meets security from the perspective of preventing inside attacks and outside attacks.Finally,the experimental results of the example protocols on the International Business Machines(IBM)quantum platform are consistent with the theoretical results.Our research indicates that our protocol is universal to a certain degree and easy to perform.

Batch Secret Sharing for Secure Multi-party Computation in Asynchronous Network

This paper proposes an efficient batch secret sharing protocol among n players resilient to t 〈 n/4 players in asynchronous network. The construction of our protocol is along the line of Hirt＇s protocol which works in synchronous model. Compared with the method of using secret share protocol m times to share m secrets, our protocol is quite efficient. The protocol can be used to improve the efficiency of secure multi-party computation （MPC） greatly in asynchronous network.