The Internet of Things (IoT) is transforming lives, making daily tasks more convenient and efficient than ever before. However, the true potential of IoT can only be realized if its nodes interact with robust security, ensuring that sensitive data and personal information remain protected. While many data transmission protocols have been proposed in recent years, most fail to deliver on their security promises in real-world scenarios. Adversaries can exploit vulnerabilities through sophisticated leakage attacks such as side channel attacks or cold boot attacks to compromise encryption keys and undermine system integrity. Without advanced, resilient protocols, the promise of IoT is put at serious risk. In addition, the corresponding protocols deployed in IoT should enjoy high computational efficiency, because the mobile terminals have weak computing power. Also, from the viewpoint of actual application, the proposed data transmission protocol should have wider universality and can be used in multiple scenarios of IoT. Therefore, to further address the above problems, we propose a general construction of a leakage-resilient data transmission protocol with multi-receiver from an identity-based hash proof system (IB-HPS) and identity-based signature (IBS) scheme. For our proposal, the unforgeability, anonymity, and confidentiality can be proved based on the security of the underlying cryptography tools. Compared with the existing protocols, our proposal has better performance, such as dynamic anonymity, leakage resilience, traceability, etc. Furthermore, to guarantee the highest levels of security and efficiency, we have developed a novel IB-HPS construction that offers continuous leakage resilience and perfect key updates. Alongside this, our new leakage-resilient IBS scheme delivers the computational efficiency essential for practical deployment in IoT networks, ensuring that security enhancements do not come at the expense of performance. Both performance analysis and security analysis show that our data transmission protocol is secure, efficient, and highly practical.

Skyline optimization is a powerful tool for filtering prominent data to support analysis and decision-making. However, traditional centralized skyline predicates are inadequate for contemporary data islands, and shallow data federation poses a threat to privacy with sensitive data. In existing distributed environments, achieving both efficiency and security in skyline computation remains a critical challenge. This paper addresses the challenge of performing secure skyline predicates on encrypted data federation while safeguarding both the dataset and skyline from unauthorized access. We propose a novel asynchronous structured skyline predicate based on vertical dominance and truth-value conversion, taking full advantage of distributed computing. Furthermore, we introduce a secure optimization that balances security and efficiency, thereby facilitating a distributed skyline predicate. We evaluate the efficiency and scalability across various parameters, demonstrating improvements in traversal overhead and expensive ciphertext operations.

Face recognition (FR) brings convenience to peoples lives while also posing security risks. Some malicious users employ FR attacks to impersonate the identity of a target. To reveal the security risks, recent work has attacked black-box FR models by utilizing substitute models to generate adversarial face images that are misclassified as the target individual due to the attack transferability of substitute models. However, the substitute models cannot accurately approximate the target model that leads to a decrease in FR attack success rate and adversarial face image quality. To address the issue, we propose the PPOM-Attack, a substitute model-free Perturbation Prediction and Optimization Method for black-box adversarial Attack against face recognition. PPOM-Attack directly obtains feedback from the target model instead of using substitute models, it avoids any discrepancy with the attack objective. To achieve this goal, we design a proximal policy optimization (PPO)-based agent to predict the perturbation regions in the face image and self-adaptively disturb the regions. To maintain high-quality adversarial face images, we further propose a minimum brightness offsets method specifically designed to generate perturbations that minimize the feature embedding difference between the adversarial and targeted face images. The experimental results show that our approach outperforms state-of-the-art FR attack methods by an average of 21.7% in terms of attack success rate, while achieving better image quality on seven FR models.

With the rapid advancement of the Internet of Things (IoT), its applications are becoming increasingly essential in actual application. Specifically, the recent surge in electric vehicles has spurred significant advancements in vehicle ad hoc networks (VANET). Therefore, efficient data transmission is a critical challenge in IoT, especially VANET. The certificateless signcryption (CLS) scheme has high authentication efficiency, which has become increasingly important in IoT. However, most existing schemes rely on bilinear pairing, resulting in high calculation costs or failure to achieve their claimed security. Furthermore, the security of some CLS schemes is proved in the random oracle, which limits the usefulness of the CLS scheme. To further address these issues, we propose an efficient and secure CLS scheme in the standard model for VANET, which enhances its practical applicability in VANET. Our construction employs formal security proofs and cost simulations to ensure the scheme’s security and low calculation costs, making it suitable for VANET. Finally, the security and efficiency analyses prove that our scheme offers enhanced security features and superior performance compared to existing schemes.

The Industrial Internet of Things (IIoT) is changing our way of life and work. As the number of mobile devices connected to IIoT increases, users will face many security challenges, such as insecure communication environments. The certificateless signature (CLS) scheme can ensure the integrity and validity of data and provide secure identity authentication for the IIoT, and several pairing-free CLS schemes have been proposed in recent years. However, we find they are vulnerable to the signature forgery attack of malicious key generation centers by safety analysis, which does not realize the security they have claimed. To solve this problem, we show an improved CLS scheme that achieves anonymity and formally proves its security under the hardness of the discrete logarithm problem in the random oracle. Comprehensive performance analysis and comparison show that our scheme has less communication and computation costs with higher security, and our construction is suitable for scenarios with limited resources. Finally, we apply this scheme to construct a mutual identity authentication protocol in the healthcare IIoT environment.

Witness encryption ($\textsf {WE}$) (Garg et al., STOC 2013) allows a message to be encrypted using a description of a difficult problem (an$\textsf {NP}$instance), while anyone who knows a solution to the problem (a witness) can decrypt the ciphertext. The extreme power of this primitive comes at the cost of its elusiveness: a construction from established cryptographic assumptions is currently out of reach (Campanelli et al., PKC 2024). The question we address in this article is how to construct extractable$\textsf {WE}$from well-studied assumptions. Our first contribution is to propose a novel witness encryption scheme, namely, a$\textsf {WE}$scheme for the$\textsf {NP}$-complete arithmetic satisfiability problem from well-studied assumptions (based on bilinear pairings), whose correctness is such that anyone who knows a witness to the arithmetic satisfiability problem can decrypt the ciphertext. As our second contribution, we propose an extractable witness key encapsulation mechanism ($\textsf {WKEM}$), which is the basic component of our$\textsf {WE}$scheme. Roughly speaking, we first encode the arithmetic circuit satisfiability problem as an arithmetic quadratic program, and then generate a key and a ciphertext based on an$\textsf {NP}$instance of the arithmetic quadratic program. Anyone with the corresponding witness can decrypt the ciphertext to obtain the key. Finally, the extractability of our extractable$\textsf {WE}$relies on the extractability of$\textsf {WKEM}$and the$\textsf {EAV}$-security of the symmetric encryption scheme. At the same time, we formally prove the extractability of$\textsf {WKEM}$based on the$\textsf {NP}$-complete circuit satisfiability problem under the$\textsf {AGM}$standard assumption d-$\textsf {DLOG}$.

With the development of cloud computing and the digital transformation of the medical industry, the application scenarios and effects of smart healthcare are constantly expanding and improving. Smart healthcare plays an important role in improving service quality and medical efficiency and reducing medical costs. However, in the process of data collection, storage, and transmission, the risk of patients’ privacy being illegally accessed or leaked has become increasingly prominent. These medical data contain sensitive information about patients, and once leaked, it will violate the privacy of patients, cause medical accidents, and seriously damage the legitimate rights and interests of patients. An efficient and reliable privacy protection mechanism is the foundation and key to establishing a harmonious doctor-patient relationship and improving medical quality. Although traditional encryption methods can provide certain information security protection, they cannot balance data protection and data analysis and processing and are not suitable for intelligent medical environments that require more precise medical decisions. Therefore, we propose a hierarchical identity-based inner product functional encryption scheme with a malicious user revocation mechanism aimed at addressing the privacy and security issues of patients in smart healthcare. This solution allows specific access and analysis of data while ensuring that patients’ sensitive information is protected from infringement, enabling doctors to diagnose and treat more accurately and optimize the allocation of medical resources to the greatest extent possible. Furthermore, we have formally demonstrated the security of the scheme and conducted a security analysis and performance comparison. The results show that our solution has better performance while ensuring security and is suitable for the efficient and secure data processing requirements of smart healthcare.

With the development of cloud computing, more and more data is stored in cloud servers, which leads to an increasing degree of privacy of data stored in cloud servers. For example, in the critical domain of medical vaccine trials, where public health outcomes hinge on the analysis of sensitive patient data, the imperative to safeguard privacy has never been more pronounced. Traditional encryption methods, though effective at protecting data, often expose vulnerabilities during decryption and lack the ability to support granular data access and computation. One-way re-encryption schemes further impede the agility of data sharing, which is indispensable for the collaborative efforts of research institutions. To address these limitations, we propose a novel bidirectional re-encryption scheme for inner-product functional encryption (IPFE). Our scheme secures data while allowing computation and sharing in an encrypted state, preserving patient privacy without hindering research. By harnessing inner-product functional encryption, our approach allows authorized researchers to extract valuable insights from encrypted data, significantly enhancing privacy protections. Our scheme’s security is predicated on the$l$-ABDHE (augmented bilinear Diffie-Hellman exponent) assumption, ensuring robustness against chosen plaintext attacks within the standard model. This foundation not only secures the data but also yields compact ciphertext length, minimizing storage demands. We introduce a protocol specifically designed for medical vaccine trials, which leverages our bidirectional IB-IPFRE (Identity-Based Inner-Product Functional Re-Encryption) scheme. This protocol enhances data security, supports collaborative research, and maintains patient privacy. Its application in vaccine trials demonstrates the scheme’s effectiveness in protecting sensitive information while enabling critical research insights.

Outsourcing data storage and computations to cloud servers offers a cost-effective solution for remote data management and query processing. However, ensuring the privacy of sensitive information remains a critical concern, and existing secure algorithms rely on data completeness where all attribute values are valid to ignore the dominance issues under intransitivity and cyclicity. This paper addresses the challenge of executing secure skyline predicates on outsourced incomplete data, while keeping the dataset, queries, and results confidential from the cloud servers. We propose a novel secure dominance under incomplete data as a core component of various query types. To balance security and efficiency, we introduce two filtering methods around access patterns. Additionally, we present two secure skyline extensions concerning dimension and skyband to produce meaningful skylines. The proposed solutions are empirically evaluated for efficiency and scalability on diverse datasets, demonstrating the practical viability of our approach.

Witness encryption (WE) allows a ciphertext to be encrypted under an NP problem such that anyone holding a valid witness for that problem can decrypt it. However, existing WE schemes or constructions are either impractical or implement only a part of these WE features. We provide two versions of the scheme. The WE scheme that 1) is based on pairings with the property of flexible decryptors, 2) only applies to some SAT problems, and 3) does not require the decryptor’s communication with an encryptor. The SAT problem can only be partially supported as it is not fully protected against all mixed-input attacks. So We provide another version of the scheme. The Modified Witness Encryption (MWE) scheme via non-interactive Oblivious Transfer (OT) 1) is based on pairings with the property of flexible decryptors, 2) applies to all SAT problems, and 3) still requires the decryptor’s or third-party communication with an encryptor, and the communication can only perform a fixed asynchronous amount of computation at regular intervals, regardless of the number of ciphertexts. At present, there is no witness encryption scheme that can satisfy both non-interactivity and (1), (2) in MWE features and there are many interactive application cases in the application scenarios of witness encryption.

With the limited resources of the Internet of Things (IoT), security and efficiency have become a challenge. The hybrid signcryption scheme is a proper method to ensure data security and integrity. Recently, through continuous and in-depth research, the signcryption scheme has made many achievements, but there are still some problems. Most proposals use bilinear mapping, which reduces computational efficiency. It brings a heavy burden to the resource-constrained IoT. And many constructions cannot meet the claimed security, causing the leakage of private messages. Therefore, we propose a lightweight certificateless hybrid signcryption (CLHS) scheme with better performance and higher security to solve the above problems. First, we have reduced storage pressure and improved computational efficiency by using certificateless public-key cryptography and elliptic curves instead of bilinear maps to construct a signcryption scheme, and the construction is lightweight. At the same time, to securely transmit messages of different lengths, we employ a hybrid signcryption scheme. Through formal security proof, efficiency, and performance analysis, our proposal has the security requirements of confidentiality and unforgeability and has better computational efficiency and security performance. Finally, we propose a secure data transmission protocol based on our CLHS scheme in Smart Grid, which inherits the advantages of high computational efficiency and better security of the underlying CLHS scheme.

In recent years, the development of Internet of Things technology has led to a surge in interest in the vehicular ad hoc network (VANET), which has greatly improved travel efficiency and facilitated vehicle data sharing. To ensure the privacy and security of both vehicles and personnel, researchers have proposed various measures for securing VANET systems. Recently, certificate-based aggregate signature (CBAS) schemes have been proposed to design an identity authentication protocol for the VANET to prevent tampering and corruption of private vehicle data. In this study, we conduct a security analysis of the previous CBAS scheme by presenting a security attack. Afterward, we propose a novel and concrete construction of the CBAS scheme that offers improved performance for VANET, which can enhance protection in the VANET scenario. We also demonstrate its security based on standard cryptographic assumptions. Comparative results from theoretical analysis and experimental evaluation illustrate the practicality of our proposed scheme. Similarly, the identity authentication protocol created based on the above CBAS scheme has the properties of dynamic anonymity, mutual identity authentication, unforgeability.

With the rapid popularization of electric vehicles (EVs) in modern society, vehicle-to-grid (V2G) has been widely concerned as an emerging technology. However, various privacy and security issues arise frequently in the energy interaction between EVs and the smart grid (SG), such as the lack of secure authentication and disclosure of EVs’ identity. Although many crypto-based schemes are proposed to achieve secure authentication of V2G networks, they rely on certificate authority (CA) or private key generator (PKG). In response to this problem, some certificateless signature-based schemes have been proposed. Nevertheless, most of them are not suitable for V2G networks due to the high computational cost and communication overhead, and they do not consider the problem of tracking illegal signatures. Therefore, we propose a traceable and privacy-preserving authentication scheme with supporting batch verification for energy trading in V2G networks. We use the method of binary tree level traversal to quickly track EVs with illegal signatures, which can reduce computational resources. Besides, the proposed scheme is easier to be deployed in real world because of avoiding the problems of key escrow and certificate management. Finally, we conduct a comprehensive security analysis and performance evaluation regarding our scheme. We prove that our proposed scheme is secure under the random oracle model (ROM), and the experimental results illustrate that the proposed scheme has less computational cost and communication overhead as compared to the existing schemes.

Polynomial commitment schemes (PCSs) are fundamental components that can effectively solve the problems arising from the combination of Internet of Things and blockchain. These allow a committer to commit to a polynomial and then later evaluate the committed polynomial at an arbitrary challenge point along with a proof of valid, without revealing any additional information about the polynomial. Recent works have presented polynomial commitment schemes based on the discrete logarithm assumption. Their schemes do not require a trusted setup, and the verifier uses homomorphism to check the polynomial evaluation proofs. However, these schemes require two-party interactions and satisfy only special soundness and special honest verifier zero-knowledge, which are infeasible for some nonsimultaneous online or decentralized applications. In this article, we propose a novel PCS inspired by the idea of the Fiat–Shamir heuristic. Our scheme is noninteractive between the committer and the verifier. Instead of waiting for the challenge values from the verifier, the committer generates the values by accessing a random oracle. Moreover, it satisfies computational soundness and zero-knowledge by using a group operation to enhance the unpredictability of challenge values. We also propose a trapdoor commitment scheme to ensure the honest use of challenge values by the committers. Finally, we present the security and performance analysis of our scheme, which shows that our scheme is feasible with an acceptable time overhead.

With the rapid development of Internet of Things (IoT) technology and the growth of traffic demand, vehicular ad hoc networks (VANETs) will become a major component of intelligent transportation systems. However, identity authentication in VANETs has emerged as a crucial challenge in maintaining communication security. The existing identity authentication methods have complex certificate management and key escrow problems, and many authentication schemes can not resist common attacks and are inefficient. Also, the above proposals cannot meet the lightweight needs of the IoT. At the same time, vehicles in the VANETs may move between different network domains, which will lead to an increase in cross-domain identity authentication requirements. Therefore, this article proposes an efficient and lightweight cross-domain direct identity authentication protocol. We apply the unpaired certificateless signature scheme to the cross-domain authentication and generate cross-domain communication credentials for the vehicles in advance in the local domain, which will greatly improve the efficiency of cross-domain authentication between the vehicles. In addition, we demonstrate the security of our protocol and further validate the security of the protocol through the simulation experiments. Finally, experiments show that our protocol is more efficient than the existing authentication protocols.

MOTIVATION: Whole-genome sequencing (WGS) is increasingly used to aid the understanding of Mycobacterium tuberculosis (MTB) transmission. The epidemiological analysis of tuberculosis based on the WGS technique requires a diverse collection of bioinformatics tools. Effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts. RESULTS: Here, we present TransFlow (Transmission Workflow), a user-friendly, fast, efficient and comprehensive WGS-based transmission analysis pipeline. TransFlow combines some state-of-the-art tools to take transmission analysis from raw sequencing data, through quality control, sequence alignment and variant calling, into downstream transmission clustering, transmission network reconstruction and transmission risk factor inference, together with summary statistics and data visualization in a summary report. TransFlow relies on Snakemake and Conda to resolve dependencies among consecutive processing steps and can be easily adapted to any computation environment. AVAILABILITY AND IMPLEMENTATION: TransFlow is free available at https://github.com/cvn001/transflow. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract To ensure privacy and security of healthcare wireless medical sensor networks (HWMSNs), several concrete constructions of efficient certificateless aggregate signature (CLAS) scheme without bilinear pairing were proposed in the last few years. However, many previous constructions of CLAS scheme were found to be impractical, which either fail to meet the claimed security or contain design flaws. For example, in some of the previous proposals, any adversary can forge a valid signature on any new message. In this paper, we first demonstrate some security issues and design flaws in the previous proposals of CLAS scheme. As follows, to further address the above deficiencies, a new construction of CLAS scheme with improved security is presented, and the formal security proof is given using Forking Lemma in the random oracle model, assuming that the discrete logarithm problem is hard. Compared with the previous CLAS schemes, our construction has similar computational costs, and it provides better security guarantees. Therefore, compared with the existing solutions, our proposal with strong security and high computational efficiency is more suitable for use in HWMSNs.

Abstract Although a large number of provably secure cryptographic primitives have been proposed in the literature, many of these schemes might be broken in practice because of various leakage attacks. Therefore, the leakage resilience should be considered in designing these primitives. However, in identity-based cryptography, most of the existing leakage-resilient identity-based encryption (IBE) schemes suffer some limitations: they either resist the leakage attacks in the selective identity security model or achieve the chosen-ciphertext attack (CCA) security based on a non-static assumption. In this paper, an IBE scheme with adaptive leakage-resilient CCA security is proposed, and its security is rigorously proved in the random oracle model under a classic static complexity assumption, e.g. decisional bilinear Diffie–Hellman assumption. In our construction, all elements of ciphertext are randomly distributed in the adversary’s view. Hence, the adversary cannot obtain any useful information of the user’s private key from the given ciphertexts. Moreover, a unique property of our construction is that the leakage parameter is independent of the plaintext space, which contributes a better leakage rate.

In the vehicular ad hoc network (VANET), the communication between the vehicle and other nodes is performed in an open channel, which puts forward the requirements for its privacy security and message integrity. Most previous protocols either frequently verify identities before accepting traffic information or do not involve identity revocation mechanisms, and they may assume that third parties are fully trusted. To further solve the above problem, a certificateless-based anonymous and revocable authentication protocol for vehicle-to-vehicle communications is proposed in this article. Our construction separates the identity authentication process from the traffic message verification, which not only avoids the disadvantage of a frequent identity revocation list (IRL) checking but also reduces the overhead in communication and message authentication. These features can make “identity authentication once, broadcast efficiency” really happen. In addition, since our authentication process is based on certificateless signature, it can resist malicious key generation centers (KGCs) and road-side units (RSUs), which is very necessary for privacy-sensitive application scenarios. Finally, the performance analyses show that our proposal is superior to the existing schemes in terms of authentication speed and communication overhead.

Nowadays, as an essential technique for intelligent transportation, vehicular ad hoc networks (VANETs) has significantly improved people’s travel experience, providing richer, and smarter services for vehicles while ensuring driver safety. However, considering that VANETs are complex, some security challenges still remain, including but not restricted to privacy preserving of vehicles, authentication of messages, limited resources in computational power, and network bandwidth. To address these issues, many privacy-preserving identity authentication schemes for VANETs have been proposed recently. However, these schemes still suffer some limitations. First, their computational overheads are heavy due to the complex calculations. Second, some schemes are vulnerable to various security weaknesses, unable to resist normal attacks. Third, in some schemes, the same pseudonym keeps unchanged and it is linked to the corresponding private key of user. The consequence is that if the user wants to change its pseudonym, the corresponding private key must be changed. In order to further solve the above problems, we propose a novel privacy-preserving identity authentication protocol based on the certificateless aggregate signature scheme, allowing the user to generate a fuzzy identity to hide her real identity, and the private key can be kept unchanged even if the corresponding pseudonym is updated. Furthermore, to achieve efficiency in computation, bilinear mapping is avoided in our proposed scheme. We prove that our protocol satisfies unforgeability in the random oracle based on a classic complexity assumption. Finally, the security and efficiency analyses demonstrate that our construction enjoys more security features and better performance compared with the existing schemes.

In recent years, many researchers have applied aggregated signature techniques to resource-constrained vehicular ad hoc networks (VANETs) authentication scenarios. We reviewed two recent VANET authentication schemes based on certificateless aggregated signatures (CLASs) while demonstrating that neither of them is resistant to public key replacement attacks under their security models. An eavesdropper can successfully forge a legitimate signature to perform malicious operation. To further address the above security flaws, we propose an improved CLAS scheme for VANET. Considering that some researchers have briefly or even incorrectly used forking lemmas in their security proofs to prove an insecure CLAS scheme, we further improve and refine the security model and security proof method for CLAS, which make the proof process transparent by using general forking lemma. Based on these improvements, our scheme can resist attacks from two types of adversaries in the CLAS security model. In the final performance analysis, the improved CLAS scheme outperforms the secure-related scheme of recent years in terms of both computational and communication efficiency. Therefore, our proposal is more suitable for resource-constrained VANET environments.

In future intelligent transportation systems, vehicular ad hoc network (VANET) is a popular application. They provide early warning of dangers through wireless communication to improve road safety. Therefore, we should protect messages from leakage and changes during transmission. To ensure the security issues in the communication process, we propose a secure and effective certificateless signcryption scheme with multiple messages and multiple receivers and prove its confidentiality and unforgeability based on the hardness of the discrete logarithm problem and the computational Diffie Hellman problem. Our scheme implements the signature and encryption of multiple messages for different receivers and achieves anonymity for the receiver. Based on our signcryption scheme, we design an identity authentication and key agreement protocol applying the construction in VANET. In addition, we also point out that through comprehensive performance analysis, our proposal has higher security and efficiency of computation and communication compared to other signcryption constructions.

By collecting and analyzing patients' e-healthcare data in Medical Internet-of-Things (MIOT), e-Healthcare providers can offer alternative and helpful evaluation services of the risk of diseases to patients. However, e-Healthcare providers cannot cope with the huge volumes of data and respond to this online service. Providers typically outsource medical data to powerful medical cloud servers. Since outsourced servers are not fully trusted, a direct evaluation service will inevitably result in privacy risks concerning the patient's identity or original medical data. It is hard to hide the results of an evaluation from the single-server model unless a fully homomorphic cryptosystem is used or the patients must communicate online with the cloud multiple times in an inefficient manner. With regards to these issues, this article proposes aSecure andPrivacy-PreservingDecisionTreeEvaluation scheme (namely SPP-DTE) to achievesecure disease diagnosis classification under e-Healthcare systems without revealing the sensitive information of patients such as physiological data or the private data of medical providers such as the structure of decision trees.Our proposed scheme usesmodified KNN computation to match the similarity and preserve the confidentiality of raw dataand also appliesmatrix randomization and monotonically increasing and one-way functions to confuse the intermediate results. The experiment is conducted in data sets from UCI machine learning repository of medical health data. Our analysis indicates that the proposed SPP-DTE scheme is efficient in terms of computational cost and communication overhead that is practical and efficient for privacy protection in e-Healthcare classification and diagnosis system.

To know the most recent market conditions, an e-commerce platform needs to be aware of the sales situation of its sellers’ commodities. The most recent market conditions can help to forecast future market trends and develop policies to guide sellers in reasonably allocating their inventory proportion. Statistical analysis is a fundamental approach to studying the sales situation. However, the sales data of an e-commerce platform usually has a significant volume. Therefore, outsourcing statistical analysis to cloud servers is an effective method. Nevertheless, sellers do not want their sales data leaked to anyone or any other organization. Moreover, in many circumstances, we cannot fully trust cloud servers. Thus, we need to utilize cryptographic or non-cryptographic tools to realize the above outsourcing. Secret sharing is a lightweight and powerful non-cryptographic tool to realize privacy-preserving data analysis. However, it needs secure channels to distribute secret shares. On the other hand, homomorphic encryption is a powerful cryptographic tool for designing privacy-preserving data analysis schemes. Nevertheless, these schemes usually do not allow the entity that holds the decryption key to collude with other entities. We propose a privacy-preserving and verifiable statistical analysis scheme for an e-commerce platform that combines a threshold secret sharing scheme with a verifiable threshold homomorphic encryption scheme. Our solution’s demand for secure channels is reduced by 40% ~ 60% compared with a traditional threshold secret sharing scheme, thanking the designed novel distribution model for delivering secret shares. Furthermore, our solution has a stronger ability to resist collusive attacks, keep sales data private from any entity, and ensure that the platform can only obtain the analysis results with the help of some cloud servers, alleviating the single point of trust. And meanwhile, the novel distributed model makes our solution enjoy better robustness and fault tolerance. The proposed solution is validated through security analyses, performance evaluations, and comparison analyses.

Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In this paper, we present a Privacy-Enhanced Aggregation for Federated Learning in MCS, namely PrivacyEAFL, to implement the training of federated learning under mobile crowdsensing system in terms of privacy protection of all participants. Firstly, considering that the crowdsensing server might share information with some participants to obtain and leak some local models, we design a collusion-resistant data aggregation approach by combining homomorphic cryptosystem and hashed Diffie-Hellman key exchange protocol. Secondly, we design a data encoding and aggregating method with data packing which can reduce the computation cost and communication overhead for the system. Thirdly, as the number of participants’ samples are dynamically changeable in MCS, we design a sample number protection method that can implement the security and privacy of the number of training samples owned by participants. Finally, we provide the experimental results on real-world datasets (i.e, MNIST and Car Evaluation) with crowdsensing devices underRaspberry-Pi 4BandRedmi-K30 Pro, respectively, and the results demonstrate that our scheme is more efficient and practical in secure and privacy-enhanced model aggregation for federated learning in mobile crowdsensing.

Online medical prediagnosis systems have already shown great achievement in providing the guidance of healthcare services with lower time and cost. Achieving a high-precision medical primary diagnosis system faces many severe challenges on the privacy of individual health information, the distributed storage of medical data and the diversity of the disease. In this paper, we propose an efficient and privacy-preserving framework for obtaining a pre-clinical guide model, which allows an authorized data analysis center to train a disease classifier using a combination of medical data gathered from different entities. Our proposed scheme is based on soft-margin support vector machine (SVM) which takes Taylor polynomial of exponential-loss as penalty. Our scheme achieves the following advantages: the trained model can tolerate some abnormal samples therefore has higher generalization ability, and the training process can constraint the inefficient operations in the encrypted domain thus leads to the availability of partial homomorphic encryption system. Lately, we prove that the proposed scheme achieves the goal of medical prediagnosis system construction and data without privacy leakage to data analysis center and model parameters without exposure to data providers, as well as demonstrating its utility and efficiency using real-world medical datasets.

With the rapid development of digital media technology, many people prefer to read e-books over article versions. The digital publishing platform can collect and analyze massive amounts of readers’ reading information. The statistical analysis results can be regarded as the platform's digital assets based on which it provides paid services for its users. However, three privacy issues are related to readers’ reading information, users’ statistical preferences, and the platform's digital assets. This article proposes a data-matching-based privacy-preserving statistic scheme. The proposed solution combines bloom filters, secret sharing, and perturbing technologies to realize an efficient match between users’ statistical preferences and massive readers’ corresponding reading information and statistical analysis of the matching results without compromising the privacy of different parties. Besides, the proposed solution adopts an edge computing paradigm to realize the process of massive data in a divide-and-conquer parallel way. It introduces the concepts of Mirror Secret Shares and Buddy Edge Devices to virtualize the$(m+1, m+1)$-threshold secret sharing scheme to an$(m+1, m+\lfloor m/2 \rfloor +2)$-threshold secret sharing scheme for achieving good robustness without adding hardware devices. The detailed analyses show that our solution meets the defined design goals. Furthermore, the experimental results demonstrate the efficiency of the proposed work.

Abstract Secret key leakage has become a security threat in computer systems, and it is crucial that cryptographic schemes should resist various leakage attacks, including the continuous leakage attacks. In the literature, some research progresses have been made in designing leakage resistant cryptographic primitives, but there are still some remaining issues unsolved, e.g. the upper bound of the permitted leakage is fixed. In actual applications, the leakage requirements may vary; thus, the leakage parameter with fixed size is not sufficient against various leakage attacks. In this paper, we introduce some novel idea of designing a continuous leakage-amplified public-key encryption scheme with security against chosen-ciphertext attacks. In our construction, the leakage parameter can have an arbitrary length, i.e. the length of the permitted leakage can be flexibly adjusted according to the specific leakage requirements. The security of our proposed scheme is formally proved based on the classic decisional Diffie–Hellman assumption.

Abstract Leakage of private information, such as the secret keys, has become a threat to the security of computing systems. It has become a common requirement that cryptographic schemes should withstand various leakage attacks, including the continuous leakage attacks. Although some research progresses have been made toward this area, there are still some unsolved issues. In the literature, the public-key encryption (PKE) constructions with (continuous) leakage resilience normally require the upper bound of leakage to be fixed. However, in many real-world applications, this requirement cannot provide sufficient protection against leakage attacks. In order to mitigate these problems, this paper demonstrates how to design a leakage amplified PKE scheme with continuous leakage resilience and chosen-plaintext attacks security. In our proposed PKE scheme, the leakage parameter can have an arbitrary length. Moreover, the length of permitted leakage in our scheme can be flexibly adjusted according to the leakage requirements of application environment. Its security is formally proved under the classic static assumption.

Nowadays, patients' physiological data and medical records are outsourced to the medical cloud to help the patients and doctors to obtain valuable and reliable services, i.e., diagnosis decision and treatment recommendation, which provides subtle and precise guidance for medical cases. Neighborhood-based recommendation (NBR) provides an effective and vivid approach to finding like-minded nodes to generate recommendations, automating what is usually known as word of mouth. Unfortunately, both diverse medical data and outsource technology raise stringent privacy concerns since the patient data is generally associated with attribute sets which contain somewhat sensitive information, such as age, gender, epidemic, physiological data and clinic result etc. The great privacy-revealing risk will occur if this sensitive medical information is maliciously exploited by network eavesdroppers, untrusted cloud servers or doctors. Considering the above privacy-revealing issues in medical data, this article proposes a privacy-preserving optimization of neighborhood-based recommendation scheme (namely, PPO-NBR), which achieves a secure and privacy-preserving recommendation for medical-aided diagnosis and treatment without revealing the patients' sensitive information. We propose a privacy-preserving medical graph construction protocol and a medical treatment recommendation protocol to implement the secure recommendation on the encrypted medical data, in which it employs BGN Cryptosystem as the building modular to encrypt data and then creatively applies graph theory to expand neighbors to search the best similar features, and uses BLS signature to ensure the authentication, and meanwhile, it deploys a ( t,n) oblivious transfer protocol to preserve the confidentiality of recommendation. A couple of experiments are implemented to evaluate the performance in terms of computational costs and communication overheads and indicate that our scheme is efficient and practical for medical-aided diagnosis.

Abstract Lossy trapdoor functions (LTFs), introduced by Peikert and Waters (STOC’08), have already been found to be a very useful tool in constructing complex cryptographic primitives in a black-box manner, such as one-way trapdoor functions, deterministic public-key encryption, CCA-secure public-key encryption, etc. Due to the existence of the side-channel attack, the leakage of trapdoor information in lossy trapdoor function systems can lead to the impossibility of provable security. Recently, Zhang et al. introduced a model of consecutive and continual leakage-resilient and updatable lossy trapdoor functions (ULTFs) and provided a concrete construction to achieve the security. Meanwhile, they proposed a consecutive and continual leakage-resilient public-key encryption scheme. However, in this paper, we demonstrate that the correctness of injective function can not be satisfied. Furthermore, the attacker can easily distinguish the evaluation key of ULTFs generated by the challenger according to the security model. Finally, we show two new constructions based on the continual leakage-resilient public-key encryption scheme of Brakerski et al. (FOCS 2010) and demonstrate the security of our scheme in the consecutive and continual leakage model.

Equipped with mobile devices, people relied on location-based services (LBSs) can expediently and reasonably organize their activities. But location information may disclose people's sensitive information, such as interests and health status. Besides, the limited resources of mobile devices restrict the further development of LBSs. In this article, aiming at the fair meeting position determination service, we design a lightweight privacy-preserving solution. In our scheme, mobile users only need to submit service requests. A cloud server and a location services provider are responsible for service response, where the cloud server achieves most of the calculation, and the location services provider determines the fair meeting location based on the computational results of the cloud server and broadcasts it to mobile users. The proposed scheme adopts homomorphic encryptions and random permutation methods to preserve the location privacy of mobile users. The security analyses show that the proposed scheme is privacy preserving under our defined threat models. Besides, the presented solution only needs to calculate $n$ Euclidean distances, and hence, our scheme has linear computation and communication complexity.

With the help of patients' health data, e-healthcare providers can offer reliable data services for better medical treatment. For example, the clinical pathway provides optimal detailed guidance for the clinical treatment. However, the e-healthcare providers are incompetent with the huge volumes of e-healthcare data, and a popular and feasible solution is used to outsource the medical data to powerful cloud servers (CSs). Because the medical data are very sensitive yet the outsourced servers are not fully trusted, the straightforward execution of clinical pathway query service will inevitably bring huge privacy risks to patients' data. Apart from the privacy issues, the efficiency issues also need to be taken into consideration, such as the communication overhead and computational cost between servers and providers. Considering the above issues, this article proposes a privacy-preserving optimization of clinical pathway query scheme (PPO-CPQ) to achieve the secure clinical pathway query under e-healthcare CSs without revealing neither the private information of patients, such as name, gender, age, and physical index, nor the sensitive information of hospitals, such as treatment, medication, and expense. In our proposed scheme, it first designs secure and privacy-preserving several subprotocols, such as privacy-preserving comparison, privacy-preserving clinical comparison, privacy-preserving stage selection, and privacy-preserving stage update protocol, to ensure privacy in the e-healthcare system, then it adopts the greedy algorithm in a secure manner to perform the query and the min-heap technology to improve efficiency. The experimental result shows that our scheme is practical and efficient in terms of computational cost and communication overhead.

Combinatorial auctions can be employed in the fields such as spectrum auction, network routing, railroad segment, and energy auction, which allow multiple goods to be sold simultaneously and any combination of goods to be bid and the maximum sum of combinations of bidding prices to be calculated. However, in traditional combinatorial auction mechanisms, data concerning bidders’ price and bundle might reveal sensitive information, such as personal preference and competitive relation since the winner determination problem needs to be resolved in terms of sensitive data as above. In order to solve this issue, this paper exploits a privacy-preserving and verifiable combinatorial auction protocol (PP-VCA) to protect bidders’ privacy and ensure the correct auction price in a secure manner, in which we design a one-way and monotonically increasing function to protect a bidder’s bid to enable the auctioneer to pick out the largest bid without revealing any information about bids. Moreover, we design and employ three subprotocols, namely, privacy-preserving winner determination protocol, privacy-preserving scalar protocol, and privacy-preserving verifiable payment determination protocol, to implement the combinatorial auction with bidder privacy and payment verifiability. The results of comprehensive experimental evaluations indicate that our proposed scheme provides a better efficiency and flexibility to meet different types of data volume in terms of the number of goods and bidders.

Machine learning plays an important role in constructing intrusion detection models. However, the information era is an era of data. With the continuous increase in data size and the growth of data dimensions, the ability of a single classifier is becoming limited in predicting samples. In this paper, we present an ensemble method using random subspace in which an extreme learning machine (ELM) is chosen as the base classifier. To optimize the ensemble model, an ensemble pruning method based on the bat algorithm (BA) is proposed. Meanwhile, a fitness function based on the accuracy and diversity of an ensemble is defined in the BA to obtain an improved classifier subset. Three public datasets, the KDD99, NSL and Kyoto datasets, are adopted to assess the robustness of the method. The empirical results indicate that the ensemble method based on random subspace can improve the accuracy and robustness over the use of an individual ELM. The results also show that compared with when all the sub-classifiers are used in the ensemble, the pruning framework can not only achieve comparable or better performance but also save substantial computing resources in an intrusion detection system (IDS).

In order to provide a flexible access control in a secure manner in open networks, Attribute-Based Encryption (ABE) implements a fine-grained decentralized access control that is based on properties or attributes a user/node owns, which has paid more attention to the applications in large-scale and dynamic networks such as Mesh network, Wireless Body Area Networks (WBAN), and Internet of Things etc. However, as the openness and exposure in such networks, an attacker (e.g., virus, eavesdropper or sniffer) can blow the concrete implementation of cryptosystems, for example side channel attacks, and then obtains some sensitive and secret states in the system by monitoring pseudo-random numbers, internal results and secret keys and thus breaks the provable security of the systems. In this paper, in order to tolerate the possible key leakage, we model a fine-grained attribute revocable attribute-based encryption, namely ADR-lrABE, and then give the concrete construction, security analysis and resilient-leakage performance. The scheme tolerates the key of matching the challenge ciphertext to be partially revealed (i.e., key leakage resilience), and it provides a update mechanism to tolerate continual leakage that allows the attacker gains the leakage beyond the bound in the lifetime of the system (i.e., continual leakage tolerance). Also, it supports the properties of attribute direct revocation that the revocation procedure does not affect any other user’s secret key. That is, the proposed scheme is proven to be semantically secure even the decryption key is partially leaked to the attacker. We analyze the leakage-resilient performance of our scheme, and indicate that the scheme achieves approximate (82 + o(1)) fraction of the bits of a decryption key being leaked. We also provide a mechanism to transform the scheme into a prime-order group. To the best of our knowledge, our schemes are the first ABE that support attribute direct revocation mechanism in the presence of key leakage in noise channel or memory leakage environments.

Recently, Rahulamathavan et al. propose a privacy preserving scheme for outsourcing SVM classification. Their core contribution is a secure protocol to attain the sign of numbers in encrypted form. In this paper, we observe that Rahulamathavan et al.'s protocol will suffer from some soundness and security problems. Then, we propose a new scheme to securely obtain the encrypted numbers' sign. Theoretical analysis and experiment results show our proposed scheme can not only fix the soundness and security problems, but also achieve higher efficiency.

Efficient power management in smart grids requires obtaining power consumption data from each resident. However, data concerning user's electricity consumption might reveal sensitive information, such as living habits and lifestyles. In order to solve this problem, this paper proposes a privacy-preserving cube-data aggregation scheme for electricity consumption. In our scheme, a data item is described as a multi-dimensional data structure (l-dimensional), and users form and live in multiple residential areas (m areas, and at most n users in each area). Based on Horner's Rule, for each user, we construct a user-level polynomial to store dimensional values in a single data space by using the first Horner parameter. After embedding the second Horner parameter into the polynomial, the polynomial is hidden by using Paillier cryptosystem. By aggregating data from m areas, we hide the area-level polynomial into the final output. Moreover, we propose a batch verification scheme in multi-dimensional data to reduce authentication cost. Finally, our analysis shows that the proposed scheme is efficient in terms of computation and communication costs, suitable for massive user groups, and supports the flexible and rapid growth of residential scales in smart grids.

In mobile environments, data stored in nodes are subject to side-channel attacks such as power analysis, emitted signal, detected radiation, etc. In this work, we propose a leakage-resilient inner-product encryption that the decryption will succeed if and only if the decryption attribute vector (generate the token) meets the orthogonal encryption attribute vector (obfuscated encryption policy), that is, the match holds that the inner product of two vectors is zero. Propose scheme supports the security of attribute-hiding and leakage-resilient in the standard model. The adversary cannot only issue any token reveal query on non-match vector, but also can request at most |$\ell $|-bit information on the token-leakage query even if the queried vector matches the challenge vector. We prove the security by the technique of dual system encryption in the orthogonal subgroups, to be strongly leakage-resilient and adaptively attribute-hiding. We also deploy our scheme as a building block to devise a secure two-party point/polynomial evaluation protocol in mobility environments, in which two parties cooperate to evaluate a polynomial in the sense that their sensitive inputs of both point and polynomial are fully preserved. Finally, we assess the performance of leakage resilience including the leakage bound and the leakage fraction (LF). Analysis shows that the leakage bound is approximate |$(n-1)\log {\pi _2}$| and the LF is about |${1}/{2(1+\omega _1+\omega _3)}$|⁠, where |$n$| is the length of vector, |$\pi _2$| is the order of subgroup |$\mathbb {G}_{\pi _2}$| and |$\omega _1,\omega _3$| are the constants. We can obtain optimized LF |$1/2-o(1)$| by varying the sizes of subgroups.

Summary An error‐tolerant keyword search scheme permits to make searches on encrypted data with only an approximation of some keyword. The scheme is suitable to the case where users' searching input might not exactly match those pre‐set keywords. An error‐tolerant keyword search scheme can be generated based on a public‐key encryption scheme, in which anyone with access to a user's public key can generate the trapdoors and indexes to keywords, and only the user holding the decryption key can obtain the records it retrieves. In this paper, we first present a general framework for searching on error‐tolerant keywords based on a public‐key encryption scheme. Then, we propose a concrete scheme based on the Cramer–Shoup cryptosystem. The scheme is adaptive chosen‐ciphertext attack secure and suitable for all similarity metrics including Hamming distance metric, edit distance metric, and set difference metric. It does not require the user to construct and store anything in advance, other than the cryptosystem used to calculate the trapdoor of keywords and to encrypt data documents. Thus, our scheme tremendously eases the users' burden. What is more, our scheme is able to transform the servers' searching for error‐tolerant keywords on ciphertexts to searching for exact keywords on plaintexts. The server can use any existing approaches of exact keywords search to search plaintexts on an index table. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract A program obfuscator is a compiling algorithm that takes a program/circuit as input and generates a new garbled circuit to implement the same functionality as before while obtaining hard‐to‐understand in some sense, that is, infeasible to learn information from the garbled circuit. In order to obtain a practical application, an obfuscation should satisfy equivalent in functionality, polynomial slowdown in efficiency, and virtual black‐box in security. In this paper, we model a stronger cryptographic obfuscation that does not only achieves the obfuscated circuit obscurity but also supports input re‐key privacy. In order to implement the re‐encryption obfuscation, we at first propose a key‐privacy two‐level encryption mechanism that implicitly supports the transformation from level‐2 ciphertext into level‐1 one, which provides an efficient method to re‐encrypt the ciphertext without explicitly decryption procedure. Under the mechanism of two‐level encryption and function of re‐encryption, we construct an obfuscation of re‐encryption that takes as input a probabilistic (keys) circuit and outputs a transformed circuit. We also give the proof that the obfuscator achieves the average‐case security for the circuit family under the extended DBDH assumption and Decisional Linear assumption in the standard model. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Many attention has been attracted by a new security primitive called leakage‐resilient cryptography, which guarantees that the cryptographic system is semantic secure even if the key has been partially leaked, which is due to the side‐channel attacks that the part of secret key can be recovered. In this work, we present a leakage‐resilient functional encryption that supports unbounded extensible subset delegation, which remains provably secure even if the attacker learns some arbitrary partial information about the secret keys. The proposed scheme provides the tolerance of continual leakage in which both memory leakage and continual leakage are captured, which has many appealing applications because there are multiple secret keys per subset string by periodically refreshing the key. The security relies on the static complex assumptions that are based on the static (bilinear) subgroup decisional problems. Finally, we give the performance evaluation such as the size of public parameters, keys, ciphertexts, leakage bound, and leakage ratio under 80‐bit security standard. Copyright © 2013 John Wiley & Sons, Ltd.

Recently, Gu et al. proposed an efficient identity-based proxy signature scheme and demonstrated their scheme was provably secure in the standard model. In this paper, we show their scheme is not secure against the malicious user through proposing three concrete attacks.

Probabilistic public key encryption with equality test (PKEET), introduced by Yang et al. in CT-RSA 2010, is able to check whether two ciphertexts are encryptions of the same message under different public keys without leaking anything else about the message encrypted under either public key. PKEET schemes have many applications, for example, in constructing searchable encryption and partitioning encrypted data. Previous PKEET schemes lack a delegation mechanism for users to specify who can perform the equality test between their ciphertexts. In this paper, we propose the notion of public key encryption with delegated equality test (PKE-DET), which requires only the delegated party to deal with the work in a practical multi-user setting, and present a concrete construction in Type 2 pairing, which is provably secure under the newly introduced security notions.

Functional encryption (FE) systems provide a flexible and expressive encryption mechanism that private keys and ciphertexts are associated with attributes and predicate formulae Γ and decryption are possible whenever keys and ciphertexts are related, i.e. ⁠. In this work, we put forward a leakage-resilient FE scheme against the amount of leakage output over a hard-to-invert function family. In our scheme, the encryption policy is specified as an arbitrary monotonic formula, and the adversary can learn the arbitrary length output of the master key and the private key from any computationally irreversible function with the input (master) keys. To improve the efficiency, we employ the set of minimal sets to describe the predicate formula or access structure, and initiate the formal model of leakage-resilient FE, which is a generic extension of identity-based encryption and attribute-based encryption in the presence of key leakage with auxiliary inputs. We provide the concrete construction in bilinear groups of composite order, and prove the adaptively leakage-resilient security in the standard model based on static assumptions. Our hard-to-invert leakage resilience employs the Goldreich–Levin theorem and its extension as a hard-core value over large fields. We also give an extensional construction in the case of obtaining the hard-to-invert randomness leakage of the encryption, which uses a strong extractor to prevent leakage of randomness and a hard-to-invert encryption to prevent the leakage of the key. Finally, we analyze and discuss the stepped-up security on master leakage and continual leakage, and the lower bound of the irreversible leakage function.

Abstract Recently, Liu and Wang proposed a homomorphic signature scheme for network coding, which was claimed to resist against pollution attacks. However, we show that in Liu and Wang's scheme, after several generations, the adversary is able to launch a successful forgery attack with a high probability. Therefore, Liu and Wang's scheme is not secure. After analyzing the cause of the attack, an improved scheme is given, which can combat against the proposed attack in an efficient way. Copyright © 2015 John Wiley & Sons, Ltd.

We reformalize and recast the notion of public key encryption with equality test (PKEET), which was proposed in CT-RSA 2010 and supports to check whether two ciphertexts encrypted under different public keys contain the same message. PKEET has many interesting applications, for example, in constructing searchable encryption and partitioning encrypted data. However, the original PKEET scheme lacks an authorization mechanism for a user to control the comparison of its ciphertexts with others’. In this paper, we study the authorization mechanism for PKEET, and propose four types of authorization policies to enhance the privacy of users’ data. We give the definitions of the policies, propose a PKEET scheme supporting these four types of authorization at the same time, and prove its security based on the computational Diffie–Hellman assumption in the random oracle model. To the best of our knowledge, it is the only PKEET scheme supporting flexible authorization.

In privacy‐carrying email systems, the authors should guarantee that the email content is confidential and sometimes the sender/receiver identities are hidden. Also, they require that the key generation is flexible and manageable. In this study, first, they propose an anonymous encryption scheme that supports a partial‐order subset delegatable ability. The proposed scheme achieves the security properties of confidentiality against adaptive chosen‐plaintext attacks, anonymity against adaptive chosen‐subset attacks and computational delegation indistinguishability. Secondly, they provide a deployment application of their anonymous encryption in an interdisciplinary group email management system with flexible and fine‐grained key delegation. The deployment can achieve the privacy of message confidentiality, receiver anonymity and delegation obliviousness, which has fine‐grained security in secure email systems. Finally, they provide an extension for the chosen‐ciphertext secure scheme, and discuss the efficiency for decryption and the security level.

ABSTRACT In outsourced database, it is desirable to store sensitive data in an encrypted form to reduce security and privacy risks because the server may not be fully trusted. Several approaches have been proposed in recent literatures to efficiently support queries on encrypted databases. Most researches focus on keywords search and range query while few works study on join query because of a lack of related cryptography primitives. We propose a solution of a privacy‐preserving general join supporting both equality tests and simple non‐equality tests on ciphertexts by using the revised Boneh–Goh–Nissim encryption algorithm and a Bloom filter. Finally, we analyze its advantages and disadvantages by the comparison with existing method on the performance and the newly introduced security notions. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT With the development of modern Internet and mobile networks, there is an increasing need for collaborative privacy‐preserving applications. Secure multi‐party computation (SMPC) gives a general solution to these applications and has become a hot topic. Yao's garbled circuit approach is a leading method in designing protocols for secure two‐party computation (2PC), which is a very important base in SMPC. However, there are only few protocols obtaining the fairness of secure 2PC, and only one of them was constructed within the standard simulation framework but with very low efficiency. In this paper, we propose an efficient fair secure Yao's garbled circuit protocol within the universally composable (UC) framework. By comparing with all other fair secure Yao's protocols, our new protocol enjoys three advantages. First, our protocol is more efficient than any other fair secure Yao's protocols within the standard simulation framework. Second, our protocol is the first fair UC‐secure Yao's garbled circuit protocol, so it is more secure than other fair Yao's protocols. Third, there does not require any third party involved in our protocol; thus, it is very suitable for many applications. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT Modern cryptographic schemes are constructed under the fundamental assumption that secret keys are perfectly hidden from all possible attackers. In practice, however, keys and internal states may partially be leaked. Recently, cryptographic construction with key‐leakage resilience has been a crucial research topic. In this work, we proposed an anonymous hierarchical identity‐based encryption that can tolerate partial leakage of secret keys. Our results were as follows. First, we provided a tolerance for continual key leakage that can capture both memory leakage and continual leakage. We extended a dual‐system encryption mechanism in orthogonal subgroups to achieve key‐leakage resilience and implicitly employed an update algorithm to guard against continual leakage. Second, the delegation depth is unbounded, which means that no predetermined depth was imposed in the setup algorithm, thus making the scheme very flexible in practice. We employed a secret‐sharing approach to split the master key into multiple shares in key components corresponding to the elements. Third, we analyzed and discussed the performance of allowable leakage‐tolerance bounds and the leakage rate of the proposed scheme and gave an evaluation that attains about 40–70% leakage rate under the Advanced Encryption Standard 112 security level. Copyright © 2013 John Wiley & Sons, Ltd.

Recent research shows that many public-key or identity-based encryption schemes are vulnerable to side-channel attacks on the keys by the interaction of an adversary with a physical device. To tolerate the possible key leakage, leakage-resilient cryptography models a class of leakage output by allowing the adversary to be able to specify a computable leakage function and obtaining the partial keys or other possibly internal states from the output of function. In this article, we propose a leakage-resilient hidden-vector encryption (HVE) scheme that supports the predicate operators such as conjunction, disjunction, comparison, range query and subset query, etc. The proposed scheme is leakage-resilient attribute-hiding secure in the sense that the adversary cannot only obtain the tokens of non-match vectors but also learn amount of key information of the vector that matches the challenge vector. To the best of our knowledge, this is the first HVE that supports token-leakage resilience. We prove the security with a series of computationally indistinguishable games that uses the dual system encryption mechanism. We also analyze and discuss the performance of leakage bound parameters and leakage fraction in the practical security level. Finally, we also give an extensive scheme to achieve the security of both attribute-hiding and payload-hiding, and analyze the performance in larger alphabets.

ABSTRACT Broadcast encryption is an effective way to broadcast a message securely such that more than one privileged receiver can decrypt it. The well‐known constructions of identity‐based broadcast encryption only support bounded broadcast users that had to deploy the maximum user number in advance. This is somewhat inefficient and impractical if the broadcast user number is predetermined. In this paper, we propose an adaptively secure identity‐based broadcast encryption in the standard model that supports arbitrary number of users in broadcast set, which eliminates the size of public parameters with a constant number of group elements and obtain short ciphertexts, secret keys, and public parameters. We use the techniques of semi‐functional ciphertexts and semi‐functional keys in orthogonal subgroups to implement the boundless broadcast set and adaptive security by means of dual‐system encryption mechanism in a composite‐order group, and we prove the scheme to be fully secure without the random oracles in the static assumptions. The proposed scheme captures the properties of confidentiality, adaptive security, constant key, and short ciphertext. We also evaluate the computational costs and communication overheads and give the deployment in secure set‐top box broadcast systems. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY Signcryption is a cryptographic primitive that fulfills both the functions of digital signature and public key encryption simultaneously, at a cost significantly lower than that required by the traditional signature‐then‐encryption approach. The performance advantage of signcryption over the signature‐then‐encryption method makes signcryption useful in many applications, such as electronic commerce, mobile communications, and smart cards. In this paper, we propose an efficient signcryption scheme in the standard model. We prove that our scheme satisfies the confidentiality and strong unforgeability. Compared with the Tan's scheme, our scheme has | G | + | p | (320) bits shorter ciphertext, 2 | p | − | G | (160) bits shorter private key, and | GT | + | G | (1184) bits shorter public key. In addition, in the Tan's scheme, a user must hold two public key/private key pairs at one time if it were both sender and receiver. Our scheme only needs a public key/private key pair, which is more practical for application. Copyright © 2011 John Wiley & Sons, Ltd.

There are nearly 700 cities in China, each with their own particular characteristics of natural environment quality and rates of economic development. When people are able to choose a city to live in, they will take into account these characteristics in order to improve their lifestyles. Based on geographic information system technology and the analytic hierarchy process method, a model of the living environment suitability index (LESI) for the larger cities of China was designed. The LESI was constructed by using the factors of economic condition, convenience and livable degree, natural environment and natural resources. The LESI model was then applied to compare the living conditions in 35 major cities in China in 2007, including 30 provincial capitals and 5 sub-provincial cities. LESI values were higher for cities in southeast China than for cities in the northwest. The highest LESI values were mainly distributed along the Chinese southeast coast and the Yangtze River Delta. Cities with the lowest LESI values were found in the centre and north of China. Of the 35 cities, 4 had highly suitable living environments and 7 had unsuitable living environments.

ABSTRACT Signcryption is a cryptographic primitive that offers both confidentiality and authentication simultaneously, which combines the functionalities of signature and encryption in a provably secure manner. Indistinguishability against adaptive chosen‐ciphertext attacks (ind‐cca2) and unforgeability against adaptive chosen‐message attacks (euf‐cma2) are two important security requirements of a signcryption protocol. In a multi‐receiver signcryption with a threshold decryption scheme, the ciphertext can be decrypted and verified when arbitrary t or more receivers among the n candidate decrypters work together. Recently, Qin et al. [Security and Communication Networks, 2011] proposed an identity‐based multi‐receiver signcryption scheme with threshold decryption, and they declared that the scheme achieves ind‐cca2 and euf‐cma2 security. In this paper, we first indicate that Qin et al.'s scheme is not secure, that is, Qin et al.'s scheme is neither semantically secure against ind‐cca2 nor unforgeable against euf‐cma2. After that, we present an improved scheme to capture the security requirements. Furthermore, we construct an anonymous version that can preserve the identity privacy of the sender and receiver, and we give the performance evaluation to indicate that our scheme has lower communication overhead although it provides the identity privacy preservation. Copyright © 2012 John Wiley & Sons, Ltd.

The critical need for better tracing of lineage in scientific databases is well known. It is clear that performance is not an issue for most domain scientists - rather the functionality is more important. In this paper, we highlight the importance of maintaining multiple versions of data and tracing fine-grained lineage in support of these needs. We study alternatives for managing versions, and propose a model for the example application of protein annotations. We present query rewriting algorithms for SPJ and ASP J queries that piggy-back lineage computation with query evaluation. Our models are implemented using PostgreSQL and tested using a large, real dataset from Uniprot. We establish the validity of the approach in enabling relevant queries and study the space and time overheads. While these overheads can be high in some cases, the real gain for scientists is the novel functionality that can allow them to ascertain reliability of derived data, and foster data-driven research. To the best of our knowledge, this is the first work that can handle these types of queries for lineage tracing.