Multi-owner Secure Encrypted Search Using Searching Adversarial Networks
Searchable symmetric encryption (SSE) for multi-owner model draws much attention as it enables data users to perform searches over encrypted cloud data outsourced by data owners. However, implementing secure and precise query, efficient search and flexible dynamic system maintenance at the same time in SSE remains a challenge. To address this, this paper proposes secure and efficient multi-keyword ranked search over encrypted cloud data for multi-owner model based on searching adversarial networks. We exploit searching adversarial networks to achieve optimal pseudo-keyword padding, and obtain the optimal game equilibrium for query precision and privacy protection strength. Maximum likelihood search balanced tree is generated by probabilistic learning, which achieves efficient search and brings the computational complexity close to $\mathcal{O}(\log N)$. In addition, we enable flexible dynamic system maintenance with balanced index forest that makes full use of distributed computing. Compared with previous works, our solution maintains query precision above 95% while ensuring adequate privacy protection, and introduces low overhead on computation, communication and storage.
Code (0)
등록된 구현이 없습니다.
Tasks
Distributed ComputingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SecureRouter: Encrypted Routing for Efficient Secure Inference
Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabling cloud servers to process client inputs without decrypting them. Alt…
Private Text Classification with Convolutional Neural Networks
Text classifiers are regularly applied to personal texts, leaving users of these classifiers vulnerable to privacy breaches. We propose a solution for privacy-preserving text classification that is based on Convolutional…
ClassificationPrivacy Preservingtext-classificationText ClassificationCollaborative Homomorphic Computation on Data Encrypted under Multiple Keys
Homomorphic encryption (HE) is a promising cryptographic technique for enabling secure collaborative machine learning in the cloud. However, support for homomorphic computation on ciphertexts under multiple keys is ineff…
Dimensionality ReductionSIG-DB: leveraging homomorphic encryption to Securely Interrogate privately held Genomic DataBases
Genomic data are becoming increasingly valuable as we develop methods to utilize the information at scale and gain a greater understanding of how genetic information relates to biological function. Advances in synthetic …
IDFace: Face Template Protection for Efficient and Secure Identification
As face recognition systems (FRS) become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be re…
Face IdentificationFace Recognition