Abstract
Smart city, as a promising technical tendency, greatly facilitates citizens and generates innumerable data, some of which is very private and sensitive. To protect data from unauthorized users, ciphertext-policy attribute-based encryption (CP-ABE) enables data owner to specify an access policy on encrypted data. However, There are two drawbacks in traditional CP-ABE schemes. On the one hand, the access policy is revealed in the ciphertext so that sensitive information contained in the policy is exposed to anyone who obtains the ciphertext. For example, both the plaintext and access policy of an encrypted recruitment may reveal the company's future development plan. On the other hand, the decryption time scales linearly with the complexity of the access, which makes it unsuitable for resource-limited end users. In this paper, we propose a CP-ABE scheme with hidden sensitive policy for recruitment in smart city. Specifically, we introduce a new security model chosen sensitive policy security: two access policies embedded in the ciphertext, one is public and the other is sensitive and fully hidden, only if user's attributes satisfy the public policy, it's possible for him/her to learn about the hidden policy, otherwise he/she cannot get any information (attribute name and its values) of it. When the user satisfies both access policies, he/she can obtain and decrypt the ciphertext. Compared with other CP-ABE schemes, our scheme supports a more expressive access policy, since the access policy of their schemes only work on the ``AND-gate'' structure. In addition, intelligent devices spread all over the smart city, so partial computational overhead of encryption of our scheme can be outsourced to these devices as fog nodes, while most part overhead in the decryption process is outsourced to the cloud. Therefore, our scheme is more applicable to end users with resource-constrained mobile devices. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.
Design and Implementation of Sensitive Information Security Model based on Term Clustering
