A sanitization approach for privacy preserving data mining on social distributed environment

Lekshmy, P. L.; Rahiman, M. Abdul

doi:10.1007/s12652-019-01335-w

Cited by 22 publications

(18 citation statements)

References 24 publications

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“…Consequently, other methods that allow for precise and targeted data to be analyzed while keeping the data encrypted ought to be utilized. For instance, techniques such as Fully Homomorphic Encryption (FHE) (El-Yahyaoui and El Kettani, 2017), Secure Function Evaluation (SFE) (Lekshmy and Rahiman, 2020), and Functional Encryption (FE) (Goodrich et al, 2014) are advisable for implementation. Moreover, the partitioning of data storage devices on non-communicating data centers can aid in lessening the problems that limit the execution of traditional security techniques.…”

Section: Proposed Security Methodsmentioning

confidence: 99%

“…Data mining can also be defined as the process through which organizations turn the collected raw data into knowledge through identifying patterns. In the view of Lekshmy and Rahiman (2020), there is a growing problem with data mining and big data as mismanagement during transit or storage of data that is threatening social ethics. There is no guarantee that all organizations secure the data they have on their databases.…”

Section: Security Challenges and Privacy Issues Of Big Datamentioning

confidence: 99%

“…As Choksi et al (2015) opined, the costly nature of keeping a log of roles and building a list of authorized users is one of the biggest issues threatening big data firms from accomplishing analytical transformations. Lekshmy and Rahiman (2020) asserted that companies participating in data mining and big data analysis through cloud computing storage have to progressively focus on handling large and diverse data sets while ensuring that all the security restrictions are installed (See 4. above ).…”

Section: Granular Access Controlmentioning

confidence: 99%

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Big data management: Security and privacy concerns

Atoum¹,

Keshta²

2021

Int. j. adv. appl. sci.

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Big data has been used by different companies to deliver simple products and provide enhanced customer insights through predictive technology such as artificial intelligence. Big data is a field that mainly deals with the extraction and systemic analysis of large data sets to help businesses discover trends. Today, many companies use Big Data to facilitate growth in different functional areas as well as expand their ability to handle large customer databases. Big data has grown the demand for information management experts such that many software companies are increasingly investing in firms that specialize in data management and analytics. Nevertheless, the issue of data protection or privacy is a threat to big data management. This article presents some of the major concerns surrounding the application and use of Big Data about challenges of security and privacy of data stored on technological devices. The paper also discusses some of the current studies being undertaken aimed at addressing security and privacy issues in Big Data.

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Section: Proposed Security Methodsmentioning

confidence: 99%

Section: Security Challenges and Privacy Issues Of Big Datamentioning

confidence: 99%

Section: Granular Access Controlmentioning

confidence: 99%

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Big data management: Security and privacy concerns

Atoum¹,

Keshta²

2021

Int. j. adv. appl. sci.

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“…The notion of distributed databases is a way to solving this problem where several parties have vertical or horizontal data divisions. Cluster analysis is a regularly utilized data mining task aimed at degrading or splitting a normally multivariate group of data, to make the data items in one group more similar [35,15,17] dedicated to overcoming the challenge of data security for data mining systems distributed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The distributed systems denote that distributed processing units are interconnected and organized via networks to subtend the need for large-scale or greatperformance computing, which has received significant attention in recent decades [1112]. peer-to-peer (P2P) systems, grids [13], ad-hoc networks [14], cloud computing systems [15], and social network systems have all received a lot of attention. Nowadays, distributed systems are used for a variety of purposes, including online services, file storage, and scientific computation [16].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Data Mining Activities in Distributed Systems

Mohammad

Yasin

Salih

et al. 2021

AJRCoS

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Distributed systems, which may be utilized to do computations, are being developed as a result of the fast growth of sharing resources. Data mining, which has a huge range of real applications, provides significant techniques for extracting meaningful and usable information from massive amounts of data. Traditional data mining methods, on the other hand, suppose that the data is gathered centrally, stored in memory, and is static. Managing massive amounts of data and processing them with limited resources is difficult. Large volumes of data, for instance, are swiftly generated and stored in many locations. This becomes increasingly costly to centralize them at a single location. Furthermore, traditional data mining methods typically have several issues and limitations, such as memory restrictions, limited processing ability, and insufficient hard drive space, among others. To overcome the following issues, distributed data mining's have emerged as a beneficial option in several applications According to several authors, this research provides a study of state-of-the-art distributed data mining methods, such as distributed common item-set mining, distributed frequent sequence mining, technical difficulties with distributed systems, distributed clustering, as well as privacy-protection distributed data mining. Furthermore, each work is evaluated and compared to the others.

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