scholarly journals Secure Symmetric Keyword Search with Keyword Privacy for Cloud Storage Services

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Taek-Young Youn ◽  
Hyun Sook Rhee

As Internet services are widely used in various mobile devices, the amount of data produced by users steadily increases. Meanwhile, the storage capacity of the various devices is limited to cover the increasing amount of data. Therefore, the importance of Internet-connected storage that can be accessed anytime and anywhere is steadily increasing in terms of storing and utilizing a huge amount of data. To use remote storage, data to be stored need to be encrypted for privacy. The storage manager also should be granted the ability to search the data without decrypting them in response to a query. Contrary to the traditional environment, the query to Internet-connected storage is conveyed through an open channel and hence its secrecy should be guaranteed. We propose a secure symmetric keyword search scheme that provides query privacy and is tailored to the equality test on encrypted data. The proposed scheme is efficient since it is based on prime order bilinear groups. We formally prove that our construction satisfies ciphertext confidentiality and keyword privacy based on the hardness of the bilinear Diffie–Hellman (DH) assumption and the decisional 3-party DH assumption.

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 272 ◽  
Author(s):  
Yu Zhang ◽  
Yin Li ◽  
Yifan Wang

Public key encryption with disjunctive keyword search (PEDK) is a public key encryption scheme that allows disjunctive keyword search over encrypted data without decryption. This kind of scheme is crucial to cloud storage and has received a lot of attention in recent years. However, the efficiency of the previous scheme is limited due to the selection of a less efficient converting method which is used to change query and index keywords into a vector space model. To address this issue, we design a novel converting approach with better performance, and give two adaptively secure PEDK schemes based on this method. The first one is built on an efficient inner product encryption scheme with less searching time, and the second one is constructed over composite order bilinear groups with higher efficiency on index and trapdoor construction. The theoretical analysis and experiment results verify that our schemes are more efficient in time and space complexity as well as more suitable for the mobile cloud setting compared with the state-of-art schemes.


Author(s):  
Hanya M. Abdallah ◽  
Ahmed Taha ◽  
Mazen M. Selim

With the rapid growth and adoption of cloud computing, more sensitive information is centralized onto the cloud every day. For protecting this sensitive information, it must be encrypted before being outsourced. Current search schemes allow the user to query encrypted data using keywords, but these schemes do not guarantee the privacy of queries (i.e., when the user hits query more than once with the same keywords, the server can capture information about the data). This paper focuses on the secure storage and retrieval of ciphered data with preserving query privacy. The proposed scheme deploys the sparse vector space model to represent each query, which focuses on reducing the storage and representation overheads. And the proposed scheme adds a random number to each query vector. Hence, the cloud server cannot distinguish between queries with the same keywords, which ensures the privacy of the query. Experimental results show that the proposed scheme outperforms other relevant state-of-the-art schemes.


Author(s):  
Fei Meng ◽  
Leixiao Cheng ◽  
Mingqiang Wang

AbstractCountless 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.


Author(s):  
Tong Liu ◽  
Yinbin Miao ◽  
Kim-Kwang Raymond Choo ◽  
Hongwei Li ◽  
Ximeng Liu ◽  
...  

Author(s):  
Aleksander B. Vavrenyuk ◽  
Viktor V. Makarov ◽  
Viktor A. Shurygin ◽  
Dmitrii V. Tcibisov

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