Under the assumption of honest-but-curious cloud service provider, various cryptographic techniques have been used to address the issues of data access control and confidentiality in public cloud storage. Among which, attribute-based encryption (ABE) has been shown to be an attractive scheme. Although the technique of ABE brings in various benefits, its onerous overhead should not be ignored. In this article, based on an improved LSSS (linear secret sharing scheme) matrix expression integrated in CP-ABE (Ciphertext-Policy Attribute-Based Encryption) algorithm, we present an efficient and secure attribute-based access control scheme for the scenarios where multiple data are shared and encrypted with frequently used sub-policies. In the scheme, a user can store the parameters about a specific sub-policy in his/her first decryption, which can be reused in the subsequent data decryptions whose embedded access policies include the same sub-policy so as to significantly reduce the computation cost. Our proposed scheme is proved to be semantically secure under chosen plaintext attacks and can well preserve the confidentiality of the data sharing system. Our analysis and experimentation also show that our scheme does significantly reduce the decryption time and while trades in only very little storage overhead, and thus effectively promotes the efficiency.

Recently, truth discovery in crowdsensing systems has received considerable attention with its appealing features for extracting truthful information from multiple unreliable data sources. However, it also poses new challenges to the issues of privacy and security. On the one hand, workers' sensed data can be used to infer their privacy. On the other hand, workers may be selfish and lazy, especially in the Internet-of-Things environment, devices are usually resource constrained, so they may dishonestly execute the costly sensing task so as to reduce resource consumption, or even break the protocol to obtain illegal rewards. Although some privacy-preserving truth discovery schemes have been proposed, they still cannot achieve strong privacy protection while keeping efficiency on the worker side, and still has no efficient incentive mechanism to persuade workers to participate in the system operations. In this article, we propose an incentive-based privacy-preserving truth discovery framework, named InPPTD. By adopting the Paillier homomorphic cryptosystem and two noncolluding servers, InPPTD not only effectively protects workers' sensed data information but also preserves the privacy of these workers' weight information. Meanwhile, a weight-based incentive mechanism is introduced in InPPTD to reduce the number of lazy workers. Security and performance analysis shows that InPPTD can guarantee stronger security features, while also ensure efficiency in terms of computation and communication overhead.

Smart grid achieves reliable, efficient and flexible grid data processing by integrating traditional power grid with information and communication technology. The control center can evaluate the supply and demand of the power grid through aggregated data of users, and then dynamically adjust the power supply, price of the power, etc. However, since the grid data collected from users may disclose the user's electricity using habits and daily activities, the privacy concern has become a critical issue. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring the trusted third party. In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying local differential privacy (LDP) based on randomized response. Our scheme can achieve efficient and practical estimation of the statistics of power supply and demand while preserving any individual participant's privacy. The performance analysis shows that our scheme is efficient in terms of computation and communication overhead.

F2C (fog-to-cloud) enables service providers to rent the low-cost cloud/fog resources to publish their services, and the fog nodes, which are deployed at the edge, can provide short-latency service to users. However, new security threats come along with this new computing paradigm, where the access control and trusted payment are concerned in this work. We propose a privacy-preserving authentication scheme. By integrating k-times anonymous authentication (k-TAA) and attribute-based access control, in our proposed scheme, service providers can autonomously determine a fine-grained access policy and the maximal access times for authorized users. Thus, users who satisfy the access policy can receive benefits of this service for certain number of times without leaking any private information. Our authentication phase has a low latency because it is offloaded to the fog as what the service does. This paper presents a lightweight and trusted billing mechanism using Merkle Hash Tree (MHT), which can detect the cloud's service forgery with high probability, without costing too much of service provider's bandwidth and computation. Rigorous security analysis proves that the proposed scheme is secure against malicious users, fogs, and cloud, and the experimental results show the significant performance advantage on both the delay reduction and service providers' cost saving.

In public cloud storage services, data are outsourced to semi-trusted cloud servers which are outside of data owners' trusted domain. To prevent untrustworthy service providers from accessing data owners' sensitive data, outsourced data are often encrypted. In this scenario, conducting access control over these data becomes a challenging issue. Attribute-based encryption (ABE) has been proved to be a powerful cryptographic tool to express access policies over attributes, which can provide a fine-grained, flexible, and secure access control over outsourced data. However, the existing ABE-based access control schemes do not support users to gain access permission by collaboration. In this paper, we explore a special attribute-based access control scenario where multiple users having different attribute sets can collaborate to gain access permission if the data owner allows their collaboration in the access policy. Meanwhile, the collaboration that is not designated in the access policy should be regarded as a collusion and the access request will be denied. We propose an attribute-based controlled collaborative access control scheme through designating translation nodes in the access structure. Security analysis shows that our proposed scheme can guarantee data confidentiality and has many other critical security properties. Extensive performance analysis shows that our proposed scheme is efficient in terms of storage and computation overhead.