In order to solve the confidentiality issues such as key exposure and attribute revocation of data stored in cloud server, an advanced anonymous privacy-preserving scheme based on Ciphertext-Policy Attributed-Based Encryption (CP_ABE) was proposed by considering confidentiality of data storage and indistinguishability of access. First, the scheme constructed a forward-secure irreversible key-update algorithm to solve key exposure. On the basis of the classified user-group and the advanced Subset-Difference algorithm, fine-grained attribute revocation was implemented with the help of cloud data re-encryption algorithm. The potential interests of user would be concealed when k-anonymity l-diversity data request was introduced based on the homomorphic encryption algorithm. The backward-security of key exposure was realized on the basis of secondary encryption inserted in data response. Under the l-Bilinear Diffie-Hellman Exponent Problem (l-BDHE) assumption, selective security of the proposed scheme was proved in the standard model. The performance advantage of the proposed scheme was demonstrated respectively in terms of efficiency, key length and security.

To solve the excessive authorization problem of Android, this paper proposed a User-Friendly privacy monitoring and management Mechanism on AnDroid named UFMDroid. Proxy redirect technology was used to implement privacy-related behavior monitoring module and fine-grained resources constraint module in Android control flow. UFMDroid analyzed the existing applications on Android market and constructed a permission profile as preset by hierarchical clustering and Euclidean distance metric to filter suspicious authority. A static threat value could be provided by calculating the distance between the preset and the current permission configuration. The privacy-related behaviors of application were classified and both of the individual threat and combination threat were considered in calculating the dynamic runtime threat value. In addition, fake data mechanism was imported to prevent the application from crashing while the permission was withdrawn. The experimental results show that UFMDroid can monitor the usage of 21 different resources and it can intercept the privacy leakage behaviors in accordance with user configuration. UFMDroid can enhance the security of Android to some extent.

The privacy-preserving location-aware model based on encrypted data was proposed to solve the privacy and security problems of Location Based Service (LBS) on the basis of survey of existing models. The spatial data was encrypted by the trusted server of a third party to realize the balance between service and privacy. Meanwhile the extended Discretionary Access Control (DAC) policy was implemented in a way that users are able to selectively share their data with others according to their own privacy concerns. Finally the location based queries were realized by using the improved order-preserving encryption algorithm through data conversion, bucket division and linear mapping. Experiments and analysis show that this model enables user-centric access control and queries on encrypted spatial data. The proposed algorithm improves the computational performance.