Due to the convenience of real time monitoring and feedback, eHealth system is gaining its popularity. With the wide adoption of electronic health records (EHRs), the security issues arise at the same time. Data integrity is one of the most fundamental security requirements and many techniques have been extensively studied to provide integrity guarantee, such as digital signature. Among various signature schemes, certificateless signature enjoys the advantages of there is neither complicated certificate management nor the key escrow problem. In this paper, we study the particular security threats in eHealth systems and analyze the limitations of traditional certificateless signature schemes if being directly applied to protect data integrity. We show that there is a gap between the traditional threat model and the security threats in practice. To improve the security, we define an enhanced notion for the “normal” type adversary in certificateless signature. Then, a concrete construction secure in the enhance model is presented, which can withstand more powerful but realistic attacks. In addition, we provide experimental simulations to analyze the efficiency and security of our proposed scheme. The results demonstrate its utility in eHealth systems and other similar scenarios.

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.

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.

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.

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.

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.