An approach for prevention of privacy breach and information leakage in sensitive data mining

Prakash, M.; Singaravel, G.

doi:10.1016/j.compeleceng.2015.01.016

Cited by 39 publications

(18 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As discussed earlier, several third-party CC services provide data support, computing Privacy in data mining during distribution implies the protection of personal information or sensitive data [36].…”

Section: Motivation Modelmentioning

confidence: 99%

An innovative privacy preserving technique for incremental datasets on cloud computing

Aldeen

Salleh

Aljeroudi

2016

Journal of Biomedical Informatics

View full text Add to dashboard Cite

Cloud computing (CC) is a magnificent service-based delivery with gigantic computer processing power and data storage across connected communications channels. It imparted overwhelming technological impetus in the internet (web) mediated IT industry, where users can easily share private data for further analysis and mining. Furthermore, user affable CC services enable to deploy sundry applications economically. Meanwhile, simple data sharing impelled various phishing attacks and malware assisted security threats. Some privacy sensitive applications like health services on cloud that are built with several economic and operational benefits necessitate enhanced security. Thus, absolute cyberspace security and mitigation against phishing blitz became mandatory to protect overall data privacy. Typically, diverse applications datasets are anonymized with better privacy to owners without providing all secrecy requirements to the newly added records. Some proposed techniques emphasized this issue by re-anonymizing the datasets from the scratch. The utmost privacy protection over incremental datasets on CC is far from being achieved. Certainly, the distribution of huge datasets volume across multiple storage nodes limits the privacy preservation. In this view, we propose a new anonymization technique to attain better privacy protection with high data utility over distributed and incremental datasets on CC. The proficiency of data privacy preservation and improved confidentiality requirements is demonstrated through performance evaluation.

show abstract

Section: Motivation Modelmentioning

confidence: 99%

An innovative privacy preserving technique for incremental datasets on cloud computing

Aldeen

Salleh

Aljeroudi

2016

Journal of Biomedical Informatics

View full text Add to dashboard Cite

show abstract

“…The four main streams of Data Breaches are Ransomware, Malware, Phishing and Denial-of-Service (DoS). Even data breaches are occurring prior to 2005, many biggest data breaches reported in history in 2005 or beyond it [4]. As every data are being recorded in cloud the volume of Data across the world are keep on increasing day after day which gives cyber criminals bigger opportunity to expose a big volume of data in a single data breaches attempt.…”

Section: Introductionmentioning

confidence: 99%

Data Leakage Prevention System: A Systematic Report

Gowr. P*,

2019

IJRTE

View full text Add to dashboard Cite

Technology is growing exponentially and in the fast growing technology more fast processing devices are being introduced. A mobile phone could even handle high processing software and even a small SD card could store the complete data of an organization. In this fast growing it environment maintaining security of data is highly important as a small loss of data might create critical impact in the organization, hence preventing their sensitive data became a greatest challenge. Traditionally organisation implemented methods like framing policies in an organization, implementing Firewall, virtual private network at the endpoints, but still these method started lagging as the technology developed the methodology of data leakage and data theft also attained heights. Hence there was a need for some system which could prevent leakage of data and this could be done with the help of Data Leakage Prevention system (DLPs). The DLPs are capable of detect leakage in Data at any states namely Data in rest, Data in transit and Data in use which increases the need of the DLPs.This paper briefly explains about the carious DLPs available, their limitations, providing the information about the gaps in security providing enough awareness on new developers as well as researchers and professionals for developing a next generation DLP which is capable of preventing data loss.

show abstract

“…The experimental analysis is presented at the end; it shows this approach performs better over the distinct l-diversity measure, probabilistic l-diversity measure and k-anonymity with t-closeness measure [4]. The [5] proposes a singular, extra flexible generalization scheme.…”

Section: Literature Reviewmentioning

confidence: 99%

A Proposed Technique for Privacy Preservation by Anonymization Method Accomplishing Concept of K-Means Clustering and DES

Pachauri¹

2017

IJRASET

View full text Add to dashboard Cite

Privacy-Preserving Data Mining also an essential branch of the data mining and an exciting topic in privacy preservation has gain particular attention in current years. Data mining has been substantially studied and useful into numerous fields which include the Internet of Things and the business growth. However, data mining tactics additionally take vicinity critical demanding situations due to enlarged sensitive statistics disclosure and the violation of privacy. This discussion describes the privacy concern that occurs due to data mining, particularly for the national security applications. We discuss PPDM by Anonymization Method in which we use K-means clustering in order to divide the given data and DES algorithm for encryption of data in order to prevent sensitive data from attacker. Keywords: privacy preservation, PPDM, K-meanS clustering, anonymization ,DES etc. I. INTRODUCTIONPrivacy is receiving extra attention partially as of counter-terrorism and the national security. At present we have heard so much approximately countrywide protection in media. This is mainly because people are now realizing that to handle terrorism, the government may need to collect information about individuals. There been much interest in the recent on applying the sa mining used to detect patterns which are unusual , terrorist activities and the fraudulent behavior. While all applications of data mining can give profit to the humans and save lives, there also negative side to this type of method , it could be a danger to the individuals privacy . This is due to data mining tools are present on the Web or, and even naive individuals can use these tools to mine information from stored data in various databases and files, and therefore violate the privateness of individuals. As we have stressed in papers to take out efficient data mining and mine necessary information for counter terrorism and national security, we gather all kinds of information about individuals.[1] However, this information could be a threat to individuals' privacy and civil liberties. This is causing major concern with different civil liberties unions. The aim is to carry out data mining and yet to the maintain privacy. This topic is known as privacy-preserving data mining . In this paper we use anonymization technique along with K-means clustering and DES algorithm.

show abstract

An approach for prevention of privacy breach and information leakage in sensitive data mining

Cited by 39 publications

References 21 publications

An innovative privacy preserving technique for incremental datasets on cloud computing

An innovative privacy preserving technique for incremental datasets on cloud computing

Data Leakage Prevention System: A Systematic Report

A Proposed Technique for Privacy Preservation by Anonymization Method Accomplishing Concept of K-Means Clustering and DES

Contact Info

Product

Resources

About