Key-Aggregate Searchable Encryption (KASE) for Group Data Sharing via Cloud Storage

Cui, Baojiang; Liu, Zheli; Wang, Lingyu

doi:10.1109/tc.2015.2389959

Cited by 161 publications

(238 citation statements)

References 20 publications

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“…Secondly, the trapdoor size is linear in the number of files. Cui et al [15] adapt the idea in [14] and propose a keyaggregate searchable encryption scheme. In their scheme, a data owner provides a single aggregated key that contains information about all files that a user is authorized to search.…”

Section: Our Contributions Our Contributions Are As Followsmentioning

confidence: 99%

“…A.1 Vulnerability of the Scheme in [15] Cui et al [15] proposed a scheme that provides an aggregation method on files for a user. Basically, each user's secret key is mapped to an aggregated number of files.…”

Section: A Appendixmentioning

confidence: 99%

“…Another weakness of the scheme in [15] happens in producing a trapdoor. Basically, the DO computes a secret key for a user by computing k agg = Q j2S g n+1 j , where 2 Z p is the secret key of the DO, and any subset S ✓ {1, .., n} which contains the indices of documents that the user is authorized to search.…”

Section: A Appendixmentioning

confidence: 99%

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Efficient Encrypted Keyword Search for Multi-user Data Sharing

Kiayias

Oksuz

Russell

et al. 2016

Lecture Notes in Computer Science

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Abstract. In this paper, we provide a secure and e cient encrypted keyword search scheme for multi-user data sharing. Specifically, a data owner outsources a set of encrypted files to an untrusted server, shares it with a set of users, and a user is allowed to search keywords in a subset of files that he is authorized to access. In the proposed scheme, (a) each user has a constant size secret key, (b) each user generates a constant size trapdoor for a keyword without getting any help from any party (e.g., data owner), independent of the number of files that he is authorized to search, and (c) for the keyword ciphertexts of a file, the network bandwidth usage (from the data owner to the server) and storage overhead at the server do not depend on the number of users that are authorized to access the file. We show that our scheme has data privacy and trapdoor privacy. While several recent studies are on secure keyword search for data sharing, we show that they either su↵er from scalability issues or lack user privacy.

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Section: Our Contributions Our Contributions Are As Followsmentioning

confidence: 99%

Section: A Appendixmentioning

confidence: 99%

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Efficient Encrypted Keyword Search for Multi-user Data Sharing

Kiayias

Oksuz

Russell

et al. 2016

Lecture Notes in Computer Science

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“…The said need for secure storage, communication and computational complexity makes such a system impractical and inefficient. Baojiang Cui, Zheli Liu, and Lingyu Wang el at [1] proposed concept of key-aggregate searchable encryption to tackle these challenges. The proposed KASE system can be applied to any storage on cloud that have the functionality of group data sharing and searching, which means that any selected group of files may selectively shared with a group of selected users by any user, while allowing to perform keyword search over the data.…”

Section: Review Of Literaturementioning

confidence: 99%

A Survey on Leakage-Resilient KASE for Group Data Sharing with Auxiliary Input via Cloud Storage

Bhagat¹,

Buchade²

2017

International Journal of Advanced Research in Computer and Comm

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Abstract:As cloud computing becomes popular, more and more sensitive information are being centralized into the cloud nowadays. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task because all the data which we outsource are in encrypted format. In this paper, we have discussed a few approaches for sharing data on cloud with minimal usage of key. We also discuss few data searching technique over encrypted data. While enjoying the advantage and convenience of cloud storage for data sharing, users are becoming more worried about careless leak of data in the cloud. To address this problem we studied various leakage models.

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“…A spammer does not need to get permissions from recipients when sending spam, which causes serious annoyance to people and even leads to information security risks [2]. If a recipient clicks a malicious link in the spam message, their personal information may be automatically sent to the spammer via a malicious program, which is an obvious challenge for privacy protection [3,4]. Statistics showed that spam accounted for 81.8% of total emails in 2016, compared with 72.9% in 2015 [5], which is obviously an increasing threat to email users.…”

Section: Introductionmentioning

confidence: 99%

CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

Liu

Zou

Zhang

et al. 2017

Wireless Communications and Mobile Computing

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Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.

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Key-Aggregate Searchable Encryption (KASE) for Group Data Sharing via Cloud Storage

Cited by 161 publications

References 20 publications

Efficient Encrypted Keyword Search for Multi-user Data Sharing

Efficient Encrypted Keyword Search for Multi-user Data Sharing

A Survey on Leakage-Resilient KASE for Group Data Sharing with Auxiliary Input via Cloud Storage

CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

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