A verifiable essential secret image sharing scheme based on HLRs(VESIS-(t,s,k,n))

In traditional secret image sharing schemes,a secret image is shared among shareholders who have the same position.But if the shareholders have two different positions,essential and non‐essential,it is necessary to use essential secret image sharing schemes.In this article,a verifiable essential secret image sharing scheme based on HLRs is proposed.Shareholder's share consists of two parts.The first part is produced by the shareholders,which prevents the fraud of dealers.The second part is a shadow image that is produced by using HLRs and the first part of share.The verification of the first part of the shares is done for the first time by using multilinear and bilinear maps.Also,for verifying shadow images,Bloom Filters are used for the first time.The proposed scheme is more efficient than similar schemes,and for the first part of the shares,has formal security.

DNATyper^(TM)21与Verifiler^(TM)Plus试剂盒对常规案件检材检验的比较

本文通过比较DNATyper^(TM)21与Verifi ler^(TM)Plus两种STR检测试剂盒对各类案件检材的检验能力,探讨DNATyper^(TM)21试剂盒在常规案件检验中应用的可靠性。取0.03125、0.0625、0.125、0.25、0.5、1.0 ng/μL基因组标准品对两种试剂盒进行灵敏度测试;应用DNATyper^(TM)21与Verifi ler^(TM)Plus试剂盒分别对1056例常规案件检材DNA进行检验;对同一样本等位基因进行一致性比较。结果表明,两种试剂盒均可以成功检测出模板浓度在0.0625 ng/μL以上的标准DNA。在检验的1056例样本中,DNATyper^(TM)21试剂盒共检出881例,检出率为83.4%;Verifi ler^(TM)Plus试剂盒检出892例,检出率为84.5%。统计学结果表明两种试剂盒检出率无统计学差异,且同一样本相同基因座得到的等位基因分型一致。综上,DNATyper^(TM)21试剂盒对各类常规检材具有良好的检验能力,可应用于日常案件检验。

VPFL:A verifiable privacy-preserving federated learning scheme for edge computing systems

Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.

新疆油田率先构建碳MRV技术体系

经过两年的推动建设,新疆油田已构建起碳MRV技术体系,碳减排量核算标准填补了行业空白,推动了碳统计监测体系的规范化、科学化。MRV技术体系包括监测、报告、核查3个组成部分。新疆油田实验检测研究院先后攻克了“缺数据、缺仪器、缺方法”三大难关,建立了天然气场站激光甲烷监测、固定顶罐呼吸气监测、涡度相关通量监测、二氧化碳注入与采出检测4类油气田碳排放源监测技术,为油气生产过程中的碳排放强度提供了可量化、可核证的技术手段。

国际航空碳抵消与减排机制对碳排放的影响研究:来自中国MRV阶段的证据

现有文献对CORSIA减排效率的研究大多基于理论分析和政策推演,尚没有基于经验数据的实证研究。文章以中国的MRV政策颁布实施的准自然实验为研究背景,选择中国航空公司2017—2021年170个观测样本作为研究对象,实证分析CORSIA计划中MRV实施阶段的政策效应和影响机制。研究发现,MRV政策的实施在整体样本上没有降低航空公司的碳强度,但样本分组后,疫情前样本组体现出较好的政策效应。进一步分组后发现,客运航空体现出更强的政策效应,大中型航空公司的政策效应的显著性明显高于小型航空公司。印证了MRV制度通过揭示、预防和建设三大职能抑制航空公司碳强度。文章进一步通过中介效应模型,揭示了环保投资和精细化管理是MRV制度促进企业提升碳绩效的传导机制和作用路径。强有力的法律约束力、公开的碳排放信息机制及MRV能力建设是保障CORSIA实现CNG2020目标的关键。文章研究为CORSIA计划的顺利实施提供了理论依据和中国经验,为行业的绿色发展提供了学术参照和“定量解”。

Verifiable Identity-Based Encryption with Keyword Search for IoT from Lattice

Internet of Things(IoT),which provides the solution of connecting things and devices,has increasingly developed as vital tools to realize intelligent life.Generally,source-limited IoT sensors outsource their data to the cloud,which arises the concerns that the transmission of IoT data is happening without appropriate consideration of the profound security challenges involved.Though encryption technology can guarantee the confidentiality of private data,it hinders the usability of data.Searchable encryption(SE)has been proposed to achieve secure data sharing and searching.However,most of existing SE schemes are designed under conventional hardness assumptions and may be vulnerable to the adversary with quantum computers.Moreover,the untrusted cloud server may perform an unfaithful search execution.To address these problems,in this paper,we propose the first verifiable identity-based keyword search(VIBKS)scheme from lattice.In particular,a lattice-based delegation algorithm is adopted to help the data user to verify both the correctness and the integrity of the search results.Besides,in order to reduce the communication overhead,we refer to the identity-based mechanism.We conduct rigorous proof to demonstrate that the proposed VIBKS scheme is ciphertext indistinguishable secure against the semi-honestbut-curious adversary.In addition,we give the detailed computation and communication complexity of our VIBKS and conduct a series of experiments to validate its efficiency performance.

Verifiable Diversity Ranking Search Over Encrypted Outsourced Data

Data outsourcing has become an important application of cloud computing.Driven by the growing security demands of data outsourcing applications,sensitive data have to be encrypted before outsourcing.Therefore,how to properly encrypt data in a way that the encrypted and remotely stored data can still be queried has become a challenging issue.Searchable encryption scheme is proposed to allow users to search over encrypted data.However,most searchable encryption schemes do not consider search result diversification,resulting in information redundancy.In this paper,a verifiable diversity ranking search scheme over encrypted outsourced data is proposed while preserving privacy in cloud computing,which also supports search results verification.The goal is that the ranked documents concerning diversification instead of reading relevant documents that only deliver redundant information.Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is effective for the diversification of documents and verification.

Verifiable Secret Redistribution for Proactive Secret Sharing Schemes

A new scheme to verifiably redistribute a secret from the old to new shareholders without reconstruction of the secret is presented in this paper. The scheme allows redistribution between different access structures and between different threshold schemes. A point worth mentioning is that this verifiable secret redistribution (VSR) scheme can identify dishonest old shareholders during redistribution without any assumption. A certain technique is adopted to verify the correctness of the old shares of the secret. As a result, the scheme is very efficient. It can be applied to proactive secret sharing (PSS) schemes to construct more flexible and practical proactive secret sharing schemes.

A Verifiable Credentials System with Privacy-Preserving Based on Blockchain

Decentralized identity authentication is generally based on blockchain, with the protection of user privacy as the core appeal. But traditional decentralized credential system requires users to show all the information of the entire credential to the verifier, resulting in unnecessary overexposure of personal information. From the perspective of user privacy, this paper proposed a verifiable credential scheme with selective disclosure based on BLS (Bohen- Lynn-Shacham) aggregate signature. Instead of signing the credentials, we sign the claims in the credentials. When the user needs to present the credential to verifier, the user can select a part of but not all claims to be presented. To reduce the number of signatures of claims after selective disclosure, BLS aggregate signature is achieved to aggregate signatures of claims into one signature. In addition, our scheme also supports the aggregation of credentials from different users. As a result, verifier only needs to verify one signature in the credential to achieve the purpose of batch verification of credentials. We analyze the security of our aggregate signature scheme, which can effectively resist aggregate signature forgery attack and credential theft attack. The simulation results show that our selective disclosure scheme based on BLS aggregate signature is acceptable in terms of verification efficiency, and can reduce the storage cost and communication overhead. As a result, our scheme is suitable for blockchain, which is strict on bandwidth and storage overhead.

Publicly Verifiable Distributed Proxy Blind Signature Scheme

In this present paper, we propose a new proxy blind signature scheme, which is publicly verifiable distributed. The algorithm uses the idea of secret sharing schemes to distribute original signer＇s ability and the power of the proxy signer, and ensure the property of publicly verifiable secret sharing schemes. A new concept ＂verifiable time period＂ is also introduced to reduce the time cost in the period of verifications and increases the efficiency of our scheme.

Information-Theoretic Secure Verifiable Secret Sharing over RSA Modulus

The well-known non-interactive and information-theoretic secure verifiable secret sharing scheme presented by Pedersen is over a large prime. In this paper, we construct a novel non-interactive and information-theoretic verifiable secret sharing over RSA （Rivest, Shamir, Adleman） modulus and give the rigorous security proof. It is shown how to distribute a secret among a group such that any set of k parties get no information about the secret. The presented scheme is generally applied to constructions of secure distributed multiplication and threshold or forward-secure signature protocols.

PVF-DA: Privacy-Preserving, Verifiable and FaultTolerant Data Aggregation in MEC

As an emergent-architecture, mobile edge computing shifts cloud service to the edge of networks. It can satisfy several desirable characteristics for Io T systems. To reduce communication pressure from Io T devices, data aggregation is a good candidate. However, data processing in MEC may suffer from many challenges, such as unverifiability of aggregated data, privacy-violation and fault-tolerance. To address these challenges, we propose PVF-DA: privacy-preserving, verifiable and fault-tolerant data aggregation in MEC based on aggregator-oblivious encryption and zero-knowledge-proof. The proposed scheme can not only provide privacy protection of the reported data, but also resist the collusion between MEC server and corrupted Io T devices. Furthermore, the proposed scheme has two outstanding features: verifiability and strong fault-tolerance. Verifiability can make Io T device to verify whether the reported sensing data is correctly aggregated. Strong fault-tolerance makes the aggregator to compute an aggregate even if one or several Io Ts fail to report their data. Finally, the detailed security proofs are shown that the proposed scheme can achieve security and privacy-preservation properties in MEC.

基于MRV模型的城市生活垃圾焚烧发电厂温室气体排放计算

城市生活垃圾焚烧发电厂具有“控制甲烷排放+代替化石燃料发电”的双重降碳功效。基于城市生活垃圾收运、处置和回收全环节的MRV模型,以北京市某生活垃圾焚烧发电厂为例,识别焚烧厂温室气体排放源,根据实际设计、运行管理经验优化MRV模型中部分温室气体排放源,计算各温室气体源排放强度及项目排放强度。建议城市生活垃圾焚烧发电厂在项目规划、设计、建设、运营等全生命周期的各阶段注入减碳理念,并基于监测、报告和核查(MRV)的程序进行温室气体排放核算。

Verifiable Secret Sharing Scheme Based on the Plane Parametric Curve

Verifiable secret sharing is a special kind of secret sharing. In this paper, A secure and efficient threshold secret sharing scheme is proposed by using the plane parametric curve on the basis of the principle of secret sharing. And the performance of this threshold scheme is analyzed. The results reveal that the threshold scheme has its own advantage of one-parameter representation for a master key, and it is a perfect ideal secret sharing scheme. It can easily detect cheaters by single operation in the participants so that the probability of valid cheating is less than 1/p (where p is a large prime).

Blockchain-based verifiable computation with optimized resource allocation

Nowadays,the data that users need to calculate and process increases sharply,however,ordinary users usually lack the required capability.Therefore,resorting to outsourcing computation,they can delegate computing tasks to high-performance nodes over the network to meet their needs.In order to ensure the correctness of outsourcing computations,a verifiable computing scheme based on the blockchain smart contract is proposed,where the primary node and the replica nodes complete the task calculation and verification respectively,and reach a final consensus on the results.Moreover,the computing resources and energy consumption of each node to make the consensus are analyzed,based on which an optimization of resources allocation is proposed to maximize the transaction throughput.The simulation results show the effectiveness of the proposed scheme built on distributed consensus and also the throughput improvement by optimizing.

Analysis and improvement of verifiable blind quantum computation

In blind quantum computation(BQC),a client with weak quantum computation capabilities is allowed to delegate its quantum computation tasks to a server with powerful quantum computation capabilities,and the inputs,algorithms and outputs of the quantum computation are confidential to the server.Verifiability refers to the ability of the client to verify with a certain probability whether the server has executed the protocol correctly and can be realized by introducing trap qubits into the computation graph state to detect server deception.The existing verifiable universal BQC protocols are analyzed and compared in detail.The XTH protocol(proposed by Xu Q S,Tan X Q,Huang R in 2020),a recent improvement protocol of verifiable universal BQC,uses a sandglass-like graph state to further decrease resource expenditure and enhance verification capability.However,the XTH protocol has two shortcomings:limitations in the coloring scheme and a high probability of accepting an incorrect computation result.In this paper,we present an improved version of the XTH protocol,which revises the limitations of the original coloring scheme and further improves the verification ability.The analysis demonstrates that the resource expenditure is the same as for the XTH protocol,while the probability of accepting the wrong computation result is reduced from the original minimum(0.866)^(d*)to(0.819)^(d^(*)),where d;is the number of repeated executions of the protocol.

End-to-end verifiable electronic voting scheme of blockchain based on random linear block code

Blockchain is an emerging decentralized technology of electronic voting.The current main consensus protocols are not flexible enough to manage the distributed blockchain nodes to achieve high efficiency of consensus.For practical implementation,the consensus based on random linear block code(RLBC)is proposed and applied to blockchain voting scheme.Along with achieving the record correctness and consistency among all nodes,the consensus method indicates the active and inactive consensus nodes.This ability can assist the management of consensus nodes and restrain the generating of chain forks.To achieve end-to-end verifiability,cast-or-audit and randomized partial checking(RPC)are used in the proposed scheme.The voter can verify the high probability of correctness in ballot encryption and decryption.The experiments illustrate that the efficiency of proposed consensus is suitable for blockchain.The proposed electronic voting scheme is adapted to practical implementation of voting.

Improvement of publicly verifiable authenticated encryption scheme

A weakness of unforgeability is found in Ma and Chen scheme, and the root cause is the susceptive linear design in the scheme. In order to avoid the weakness and susceptive linear design, an improvement by means of two mechanisms including quadratic residue and composite discrete logarithm is proposed, which can defeat the forgery attacks in Ma and Chen scheme. The new scheme remains good confidentiality, public verifiability and efficiency.

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.