ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud

Zobaed, SM; Ahmad, Sahan; Gottumukkala, Raju; Salehi, Mohsen Amini

doi:10.1109/hpcc/smartcity/dss.2019.00093

Cited by 11 publications

(16 citation statements)

References 24 publications

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“…Zobaed et al 19 introduced a novel Clust Crypt technique for efficient topic‐based clustering approach of encrypted unstructured big data in the cloud environment. This work is also based on clustering technique for encrypted data.…”

Section: Literature Reviewmentioning

confidence: 99%

Swarm intelligence‐based secure high‐order optimal density selection for industrial internet‐of‐things (IIoT) data on cloud environment

Primya¹,

Subashini

2021

Int J Communication

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Summary Industrial internet‐of‐things (IIoT) cloud computing gives users with the ease of outsourcing calculations and carrying the risk of privacy disclosure. To solve this issue, secure high‐order clustering algorithm by fast search (SHOCFS) technique is introduced recently. The major factors of SHOCFS algorithm are speed improvement and detecting optimal density peaks. The optimal selection of density points in the SHOCFS algorithm still becomes an important issue to be considered. The swallow swarm optimization (SSO) is introduced in the proposed work for choosing optimal density peaks in clustering algorithm. This paper motivates to design a new secure cloud service system for cloud in IIoT environment. The secure high‐order optimal density selection (SHODS3O‐CFS) approach is proposed in hybrid cloud environment. The major contribution of the work is to develop an optimization‐based secure clustering algorithm for finding the best density points in clustering algorithm. It may reduce the overlapping peak points in the cluster and increase the security of system in hybrid cloud. The experimental results of the proposed algorithm and existing methods are measured via clustering metrics such as accuracy, clustering center quality, and speed ratio. The metrics like clustering center quality, Rand index (RI), encryption time (ms), and speedup ratio have been used to measure the results of the proposed system and existing methods. From the results, it is concluded that the proposed work has achieved higher average RI of 93.4%, which is 29.68% and 17.4% higher when compared with the privacy preservation high‐order clustering algorithm by fast search (PPHOCFS) and SHOCFS methods.

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Section: Literature Reviewmentioning

confidence: 99%

Swarm intelligence‐based secure high‐order optimal density selection for industrial internet‐of‐things (IIoT) data on cloud environment

Primya¹,

Subashini

2021

Int J Communication

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“…Cloud-based enterprise search services, such as AWS Kendra, offer semantic searchability, given that they are provided with the plain-text data. That means the semantic ability comes with the cost of compromising the users' data privacy [10], [14], [5]. This is, in fact, the trapdoor that particularly internal attackers can misuse to breach the confidentiality or even the integrity of the users' data.…”

Section: A Cloud-based Enterprise Search Servicesmentioning

confidence: 99%

“…Although the cloud services have been fascinating for big data owners [3], there have been numerous privacy violation incidents [4] during recent years that have made individuals and businesses with sensitive data (e.g., healthcare documents) hesitant to fully embrace the data management cloud services. In one incident, confidential information of over three billion Yahoo users were exposed [5]. In another incident, information of over 14 million Verizon customer accounts were exposed from the company's cloud system [5].…”

Section: Introductionmentioning

confidence: 99%

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SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud

Zobaed¹,

Salehi²,

Buyya³

2021

Preprint

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Cloud-based enterprise search services (e.g., AWS Kendra) have been entrancing big data owners by offering convenient and real-time search solutions to them. However, the problem is that individuals and organizations possessing confidential big data are hesitant to embrace such services due to valid data privacy concerns. In addition, to offer an intelligent search, these services access the user's search history that further jeopardizes his/her privacy. To overcome the privacy problem, the main idea of this research is to separate the intelligence aspect of the search from its pattern matching aspect. According to this idea, the search intelligence is provided by an on-premises edge tier and the shared cloud tier only serves as an exhaustive pattern matching search utility. We propose Smartness At Edge (SAED mechanism that offers intelligence in the form of semantic and personalized search at the edge tier while maintaining privacy of the search on the cloud tier. At the edge tier, SAED uses a knowledge-based lexical database to expand the query and cover its semantics. SAED personalizes the search via an RNN model that can learn the user's interest. A word embedding model is used to retrieve documents based on their semantic relevance to the search query. SAED is generic and can be plugged into existing enterprise search systems and enable them to offer intelligent and privacy-preserving search without enforcing any change on them. Evaluation results on two enterprise search systems under real settings and verified by human users demonstrate that SAED can improve the relevancy of the retrieved results by on average ≈ 24% for plain-text and ≈ 75% for encrypted generic datasets.

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“…However, with big datasets, the index size can become extensively large [13] and the real-timeness of the system is affected. To make searchable encryption systems scalable, solutions are provided to partition the encrypted keywords of the index structure into several clusters based on the topical relatedness of keywords [7] [14]. Then, for a given search query, a pruning method is used to limit the search space only to clusters relevant to the query [5].…”

Section: Introductionmentioning

confidence: 99%

Edge Computing for User-Centric Secure Search on Cloud-Based Encrypted Big Data

Ahmad

Zobaed

Gottumukkala

et al. 2019

2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference On

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Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. This has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can potentially address these concerns by establishing user-centric cloud services and granting data control to the user. Nonetheless, user-side encryption limits the ability to process (e.g., search) encrypted data on the cloud. Accordingly, in this research, we provide a framework that enables processing (in particular, searching) of encrypted multiorganizational (i.e., multi-source) big data without revealing the data to cloud provider. Our framework leverages locality feature of edge computing to offer a user-centric search ability in a realtime manner. In particular, the edge system intelligently predicts the user's search pattern and prunes the multi-source big data search space to reduce the search time. The pruning system is based on efficient sampling from the clustered big dataset on the cloud. For each cluster, the pruning system dynamically samples appropriate number of terms based on the user's search tendency, so that the cluster is optimally represented. We developed a prototype of a user-centric search system and evaluated it against multiple datasets. Experimental results demonstrate 27% improvement in the pruning quality and search accuracy.

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ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud

Cited by 11 publications

References 24 publications

Swarm intelligence‐based secure high‐order optimal density selection for industrial internet‐of‐things (IIoT) data on cloud environment

Swarm intelligence‐based secure high‐order optimal density selection for industrial internet‐of‐things (IIoT) data on cloud environment

SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud

Edge Computing for User-Centric Secure Search on Cloud-Based Encrypted Big Data

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