Efficient Revocable Attribute-Based Keyword Search Against Keyword Guessing Attack
Chen Zhu, Jiale Ding, Xingrui Wei, Yang Lu, Yinxia Sun · 2024
Attribute-Based Keyword Search (ABKS) technology has changed the way data is encrypted and retrieved, enabling data owners to securely store encrypted data in the cloud and allow retrieval only by users who comply with specific access policies. To prevent malicious tampering and protect user privacy, data owners need to revoke invalid users’ access to keyword ciphertexts in cloud storage. In addition, most ABKS schemes are vulnerable to keyword guessing attacks because any entity can generate keyword ciphertexts. This paper proposes a secure and efficient revocable attribute-based keyword search (SRABKS) scheme. First, SRABKS employs a revocation mechanism that enables the data owner to revoke invalid data users by updating the access policy with the help of the cloud server. In addition, we integrate the data owner’s private key into the keyword’s ciphertexts, thus effectively preventing unauthorized entities from generating invalid ciphertexts. Under the Decision Bilinear Diffie-Herman (DBDH) assumption, we give formal proofs of the indistinguishable security of keyword ciphertexts under adaptive chosen-keyword attack (KC-IND-CKA) and the indistinguishable security of keyword trapdoors under adaptive chosen-keyword attack (TD-IND-CKA). Finally, experiments show that the computational efficiency of SRABKS is very efficient.