Big Data in the Cloud

Zobaed, SM; Salehi, Mohsen Amini

doi:10.1007/978-3-319-63962-8_40-1

Cited by 5 publications

(7 citation statements)

References 23 publications

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“…Specifically, a query is searched against the index and the result‐set, referencing the relevant documents with respect to the query, is retrieved for the user. However, using enterprise search services implies outsourcing contents to the cloud that in certain domains, such as law‐enforcement and healthcare reports, have raised serious data privacy concerns, 4,5 particularly, after numerous recent data privacy violations 6 in the cloud environments. In one incident, 7 more than 14 million Verizon customer accounts information were exposed from their cloud repository in 2018.…”

Section: Introductionmentioning

confidence: 99%

“…Client‐side encryption, 4,9 in which documents are encrypted with the user's key before outsourcing to the cloud, is a promising method to achieve the desired data privacy. In this method, only the client has the ability to decrypt the documents, hence, it can potentially provide confidentiality against both internal and external attackers 6,10 . However, the limitation of this method is the lack of processing (e.g., search and clustering) ability on the encrypted documents.…”

Section: Introductionmentioning

confidence: 99%

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Privacy‐preserving clustering of unstructured big data for cloud‐based enterprise search solutions

Zobaed

Salehi

2022

Concurrency and Computation

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Summary Cloud‐based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient search solutions over their enterprise big datasets. However, individuals and businesses dealing with confidential big data (e.g., criminal reports) are reluctant to fully embrace such cloud services due to valid data privacy concerns. Solutions based on client‐side encryption have been developed to mitigate these concerns. Nonetheless, such solutions hinder data processing, especially, data clustering, which is pivotal in applications such as real‐time search on large corpora (e.g., big datasets). To cluster encrypted big data, we propose privacy‐preserving clustering schemes, called ClusPr, for three forms of unstructured datasets, namely static, semi‐dynamic, and dynamic. ClusPr functions based on statistical characteristics of the datasets to: (A) determine the suitable number of clusters; (B) populate the clusters with topically relevant tokens; and (C) adapt the cluster set based on the dynamism of the underlying dataset. Experimental results, obtained from evaluating ClusPr against other schemes in the literature, on three different test datasets demonstrate between 30%$$ 30\% $$ and 60%$$ 60\% $$ improvement on the cluster coherency. Moreover, we notice that employing ClusPr within a privacy‐preserving enterprise search system can reduce the search time by up to 78%$$ 78\% $$, while improving the search accuracy by up to 35%$$ 35\% $$.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Privacy‐preserving clustering of unstructured big data for cloud‐based enterprise search solutions

Zobaed

Salehi

2022

Concurrency and Computation

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“…To satisfy all of the aforementioned desires of a particular user, our main idea in this research is to separate the intelligence aspect of the enterprise search from its pattern matching aspect. According to this idea, we propose to leverage onpremises edge computing [11], [12] to handle the search intelligence and user-side encryption. For that purpose, the edge-based mechanism, called Smartness At Edge (SAED), is developed to extract both contextualized and personalized semantics from the search query and the user's search history as well.…”

Section: Introductionmentioning

confidence: 99%

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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“…As such, organizations use cloud services to relieve from the burden of storing large volume of data locally. However, the outsourced contents (documents) often contain private or sensitive information (e.g., criminal or financial reports [38]) that need to be protected against internal and external cloud attackers. In fact, data security and privacy concerns have made many organizations reluctant to use cloud.…”

Section: Introductionmentioning

confidence: 99%

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

Zobaed

Ahmad

Gottumukkala

et al. 2019

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

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we present an approach named ClustCrypt for efficient topic-based clustering of encrypted unstructured big data in the cloud. ClustCrypt dynamically estimates the optimal number of clusters based on the statistical characteristics of encrypted data. It also provides clustering approach for encrypted data. We deploy ClustCrypt within the context of a secure cloud-based semantic search system (S3BD). Experimental results obtained from evaluating ClustCrypt on three datasets demonstrate on average 60% improvement on clusters' coherency. ClustCrypt also decreases the search-time overhead by up to 78% and increases the accuracy of search results by up to 35%.

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Big Data in the Cloud

Cited by 5 publications

References 23 publications

Privacy‐preserving clustering of unstructured big data for cloud‐based enterprise search solutions

Privacy‐preserving clustering of unstructured big data for cloud‐based enterprise search solutions

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

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

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