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Ciphertext-policy attribute-based encryption with hidden sensitive policy from keyword search techniques in smart city

Fei Meng, Leixiao Cheng, Mingqiang Wang

2021EURASIP Journal on Wireless Communications and Networking22 citationsDOIOpen Access PDF

Abstract

Abstract Countless data generated in Smart city may contain private and sensitive information and should be protected from unauthorized users. The data can be encrypted by Attribute-based encryption (CP-ABE), which allows encrypter to specify access policies in the ciphertext. But, traditional CP-ABE schemes are limited because of two shortages: the access policy is public i.e., privacy exposed; the decryption time is linear with the complexity of policy, i.e., huge computational overheads. In this work, we introduce a novel method to protect the privacy of CP-ABE scheme by keyword search (KS) techniques. In detail, we define a new security model called chosen sensitive policy security : two access policies embedded in the ciphertext, one is public and the other is sensitive and hidden. If user's attributes don't satisfy the public policy, he/she cannot get any information (attribute name and its values) of the hidden one. Previous CP-ABE schemes with hidden policy only work on the “AND-gate” access structure or their ciphertext size or decryption time maybe super-polynomial. Our scheme is more expressive and compact. Since, IoT devices spread all over the smart city, so the computational overhead of encryption and decryption can be shifted to third parties. Therefore, our scheme is more applicable to resource-constrained users. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.

Topics & Concepts

CiphertextComputer scienceAttribute-based encryptionEncryptionComputer securitySemantic securityAccess structureScheme (mathematics)Public-key cryptographyTheoretical computer scienceCryptographySecret sharingMathematicsMathematical analysisCryptography and Data SecurityComplexity and Algorithms in GraphsPrivacy-Preserving Technologies in Data