An extended chaotic maps-based keyword search scheme over encrypted data resist outside and inside keyword guessing attacks in cloud storage services

2015 ◽  
Vol 80 (3) ◽  
pp. 1601-1611 ◽  
Author(s):  
Chun-Ta Li ◽  
Chin-Wen Lee ◽  
Jau-Ji Shen
Keyword(s):  
Cloud Storage ◽  
Chaotic Maps ◽  
Keyword Search ◽  
Encrypted Data ◽  
Extended Chaotic Maps ◽  
Cloud Storage Services

scholarly journals Theory and application of encrypted sequential data processing: search and computation

2021 ◽  
Author(s):  
Hoi Ting Poon
Keyword(s):  
Cloud Storage ◽  
Keyword Search ◽  
Computational Cost ◽  
Bloom Filters ◽  
Sequential Data ◽  
User Privacy ◽  
Three Solutions ◽  
Encrypted Data ◽  
Storage Cost ◽  
Target Values

Cloud Computing has seen a dramatic rise in adoption in the past decade amid se- curity and privacy concerns. One area of consensus is that encryption is necessary, as anonymization techniques have been shown to be unreliable. However, the processing of encrypted data has proven to be difficult. Briefly, the goal is to maintain security over remotely stored and accessed data while achieving reasonable storage cost and perfor- mance. Search is the most basic and central functionality of a privacy-protected cloud storage system actively being investigated. Recent works have looked at enabling more specialized search functions. In this thesis, we explore the problem of searching and pro- cessing of sequential data. We propose three solutions targeting textual data, with em- phasis respectively on security, storage cost and performance. Our first solution achieves a high level of security with reduced communication, storage and computational cost by exploiting properties of natural languages. Our second solution achieves a minimal storage cost by taking advantage of the space efficiency of Bloom filters. Both propos- als were also first to enable non-keyword search in phrases. Using a subsequence-based solution, our final phrase search scheme is currently the fastest phrase search protocol in literature. We also show how sequential data search schemes can be extended to in- clude auditing with minimal additional cost. The solution is capable of achieving proof of retrievability with unbounded number of audits. A sample application which enables searching and computing over target values of encrypted XML files is also demonstrated. In terms of media, we describe an encrypted cloud media storage solution that simultane- ously protects user privacy and enables copyright verification, and is the first to achieve security against dishonest participants. We also describe a framework where practical scalable privacy-protected copyright detection can be performed. Finally, an application of sequence querying over generic data in the form of an Anti-Virus over encrypted cloud storage is demonstrated. A private scanning solution and a public Anti-Virus as a ser- vice solution are described, noting that the technique can be conceptualized as a generic pattern matching solution on encrypted data. We also include some directions on future work and unexplored applications.

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
Keyword(s):  
Open Channel ◽  
Keyword Search ◽  
Huge Amount ◽  
Encrypted Data ◽  
Diffie Hellman ◽  
Remote Storage ◽  
Query Privacy ◽  
Bilinear Groups ◽  
Equality Test ◽  
Cloud Storage Services

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.

scholarly journals Variable Techniques for Ciphertext Retrieval through Encrypted Data

2019 ◽  
Vol 8 (12) ◽  
pp. 3477-3479
Keyword(s):  
Cloud Storage ◽  
Data Privacy ◽  
Large Scale ◽  
Keyword Search ◽  
Maximum Size ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Data Utilization ◽  
Encrypted Data ◽  
Cloud Data

In recent years, Cloud computing provides strong grip and flexible access on outsource data, cloud storage, data privacy is major concern from to outsource their data, authenticated users are allowed to access this storage to prevent important and sensitive data. For data protection and utilization, we encrypt our sensitive data before outsourced our data because cannot trust storage server, are un-trusty but on other hand, data retrieval in encrypted format from cloud, is challenging task for data utilization, was encrypted from plaintext to ciphertext, when retrieves from cloud storage. However, searchable encryption schemes used Boolean search but they are unable to make data utilization for huge data and failed to handle multi-users access to retrieve ciphertext from cloud and user’s authentication. In this paper, we are using ranked keyword search over encrypted data by going k-documents at storage and using a Hierarchical Clustering Method is designed to guide more search semantics with an additional feature of making the system to cope the demand for fast ciphertext k-search in large scale environments explored the relevance score such as massive and big cloud data. This threshold splits the consequential clusters into sub-clusters until the necessity on the maximum size of cluster is reached. To make fetching search to be secure and privacy-preserving, it is built an index for searching on cloud data and retrieve the most relevant files from cloud. To defending privacy breaches from unauthorized users, users will go through authentication process and data retrieval time as well.

scholarly journals Efficient Public Key Encryption with Disjunctive Keywords Search Using the New Keywords Conversion Method

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 272 ◽  
Author(s):  
Yu Zhang ◽  
Yin Li ◽  
Yifan Wang
Keyword(s):  
Cloud Storage ◽  
Keyword Search ◽  
Public Key ◽  
Inner Product ◽  
Encryption Scheme ◽  
Public Key Encryption ◽  
Encrypted Data ◽  
Bilinear Groups ◽  
Time And Space Complexity ◽  
Selection Of

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.

scholarly journals Theory and application of encrypted sequential data processing: search and computation

2021 ◽  
Author(s):  
Hoi Ting Poon
Keyword(s):  
Cloud Storage ◽  
Keyword Search ◽  
Computational Cost ◽  
Bloom Filters ◽  
Sequential Data ◽  
User Privacy ◽  
Three Solutions ◽  
Encrypted Data ◽  
Storage Cost ◽  
Target Values

Cloud Computing has seen a dramatic rise in adoption in the past decade amid se- curity and privacy concerns. One area of consensus is that encryption is necessary, as anonymization techniques have been shown to be unreliable. However, the processing of encrypted data has proven to be difficult. Briefly, the goal is to maintain security over remotely stored and accessed data while achieving reasonable storage cost and perfor- mance. Search is the most basic and central functionality of a privacy-protected cloud storage system actively being investigated. Recent works have looked at enabling more specialized search functions. In this thesis, we explore the problem of searching and pro- cessing of sequential data. We propose three solutions targeting textual data, with em- phasis respectively on security, storage cost and performance. Our first solution achieves a high level of security with reduced communication, storage and computational cost by exploiting properties of natural languages. Our second solution achieves a minimal storage cost by taking advantage of the space efficiency of Bloom filters. Both propos- als were also first to enable non-keyword search in phrases. Using a subsequence-based solution, our final phrase search scheme is currently the fastest phrase search protocol in literature. We also show how sequential data search schemes can be extended to in- clude auditing with minimal additional cost. The solution is capable of achieving proof of retrievability with unbounded number of audits. A sample application which enables searching and computing over target values of encrypted XML files is also demonstrated. In terms of media, we describe an encrypted cloud media storage solution that simultane- ously protects user privacy and enables copyright verification, and is the first to achieve security against dishonest participants. We also describe a framework where practical scalable privacy-protected copyright detection can be performed. Finally, an application of sequence querying over generic data in the form of an Anti-Virus over encrypted cloud storage is demonstrated. A private scanning solution and a public Anti-Virus as a ser- vice solution are described, noting that the technique can be conceptualized as a generic pattern matching solution on encrypted data. We also include some directions on future work and unexplored applications.

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