The ever-growing demand for electrical energy of sensing devices in the Internet of Things (IoT) has led to generating large amounts of electricity consumption data. Electricity service providers often use wireless sensor networks to collect sensing devices' electricity consumption data for statistical analysis, so as to provide sensing devices with improved electrical services. As an important data mining technique, while data clustering excels in dealing with such massive data, it imposes the risk of privacy disclosure in the process of data clustering. In an effort of solving this problem, Blum et al. proposed a differential privacy k-means algorithm, effectively preventing privacy disclosure. However, the availability of data clustering results is reduced due to the data distortion in Blum's algorithm. In this paper, we propose a privacy and availability data clustering (PADC) scheme based on k -means algorithm and differential privacy, which enhances the selection of the initial center points and the distance calculation method from other points to center point. Moreover, PADC attempts to reduce the outlier effect through detecting outliers during the clustering process. Security analysis indicates that our scheme satisfies the goal of differential privacy and prevents privacy information disclosure. Meanwhile, performance evaluation shows that our scheme, at the same privacy level, improves the availability of clustering results compared to the existing differential privacy k-means algorithms, suggesting that our proposed PADC scheme outperforms others for intelligent electrical service in IoT.

The designs of existing incentive mechanisms in mobile crowdsensing are primarily platform-centered or user-centered, while overlooking the multidimensional consideration of sensing task requirements. To tackle the above issues, we propose a sensing task-oriented user selection incentive mechanism (TRIM) based on edge computing, in an effort toward a task-centered design framework in mobile crowdsensing. Considering the constraint of the task budget, the edge node performs a secondary user selection based on the ratio of the similarity between the sensing task and the sensing user and the expected reward of the sensing user. By adopting a secure multi-party sorting protocol, enhanced by fuzzy closeness and the fuzzy comprehensive evaluation method, the target user set is determined aiming at maximizing the similarity of the task and the users preferences, while minimizing the payment of the edge node, and ensuring the fairness of the sensing user being selected. Simulation results show that TRIM achieves feasible and efficient user selection while ensuring the privacy and security of sensing users. Among the dynamic changes of task requirements, TRIM excels with user selections reaching nearly 90% on the data quality level compliance rate and 70% on the task budget consumption ratio, superior to existing incentive mechanism.

With the rapid development of versatile cloud services, it becomes increasingly susceptible to use cloud services to share data in a friend circle in the cloud computing environment. Since it is not feasible to implement full lifecycle privacy security, access control becomes a challenging task, especially when we share sensitive data on cloud servers. In order to tackle this problem, we propose a key-policy attribute-based encryption with time-specified attributes (KP-TSABE), a novel secure data self-destructing scheme in cloud computing. In the KP-TSABE scheme, every ciphertext is labeled with a time interval while private key is associated with a time instant. The ciphertext can only be decrypted if both the time instant is in the allowed time interval and the attributes associated with the ciphertext satisfy the key's access structure. The KP-TSABE is able to solve some important security problems by supporting user-defined authorization period and by providing fine-grained access control during the period. The sensitive data will be securely self-destructed after a user-specified expiration time. The KP-TSABE scheme is proved to be secure under the decision l-bilinear Diffie-Hellman inversion (l-Expanded BDHI) assumption. Comprehensive comparisons of the security properties indicate that the KP-TSABE scheme proposed by us satisfies the security requirements and is superior to other existing schemes.