Analysis of data security by using anonymization techniques

Kaur, Preet Chandan; Ghorpade, Tushar; Mane, Vanita

doi:10.1109/confluence.2016.7508130

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…Identity disclosure is when an individual can be distinguished from the data being published. And Attribute disclosure is when the new information about any of the individual is released [4]. Identity disclosure allows the Attribute disclosure.…”

Section: A Types Regarding Information Disclosurementioning

confidence: 99%

“…The identification of the key information is protected when anonymization is performed on data. Some details in the data are confidential which needs to be hidden from the third party and all the threats [4]. Data anonymization declares all the information that is used for queries as well as analysis while it maintains the sensitive data to be kept private Demand of Anonymization of data is because of the increasing occurrences of misuse of personal data and privacy issues [8].…”

Section: Anonymization Of Datamentioning

confidence: 99%

“…Moreover, it provides better utility for the preservation of privacy. Slicing techniques slice the big data so that infrequent attributes break the collaboration among them [11]. This technique reduces the dimensionality of data.…”

Section: G Slicingmentioning

confidence: 99%

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Preservation of Privacy of Big Data Using Efficient Anonymization Technique

Rizwan

2020

Research Journal Of Computer Science And Information Technology

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Big data needs to be retained private because of the increase in the amount of data. Data is generated from social networks, organizations and various other ways, which is known as big data. Big data requires large storage as well as high computational power. At every stage, the data needs to be protected. Privacy preservation plays an important role in keeping sensitive information protected and private from any attack. Data anonymization is one of the techniques to anonymize data to keep it private and protected, which includes suppression, generalization, and bucketization. It keeps personal and private data anonymous from being known by others. But when it is implemented on big data, these techniques cause a great loss of information and also fail in defense of the privacy of big data. Moreover, for the scenario of big data, the anonymization should not only focus on hiding but also on other aspects. This paper aims to provide a technique that uses slicing, suppression, and functional encryption together to achieve better privacy of big data with data anonymization.

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Section: A Types Regarding Information Disclosurementioning

confidence: 99%

Section: Anonymization Of Datamentioning

confidence: 99%

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Preservation of Privacy of Big Data Using Efficient Anonymization Technique

Rizwan

2020

Research Journal Of Computer Science And Information Technology

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“…Some companies fairly managed to implement data anonymization on a small scale using SQL scripts efficiently to encrypt data [152]. Some other companies have failed after obtaining the best data-masking tool [153]. Anonymization consists of techniques and procedures for data processing, algorithms, keys, and data life cycle.…”

Section: B Privacy and Securitymentioning

confidence: 99%

“…However, these techniques can be applied only on at-rest or visible data i.e. logs, data exports, web pages [152], [153].…”

Section: B Privacy and Securitymentioning

confidence: 99%