Correlation Based Anonymization Using Generalization and Suppression for Disclosure Problems

Thakkar, Amit; Bhatti, Aashiyana Arifbhai; Vasa, Jalpesh

doi:10.1007/978-3-319-11218-3_52

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…This technique should be applied at the beginning of the anonymization process. This is the strongest type of anonymization technique because there is no way of recovering any information back after anonymization [7]. After the elimination of the joining date attribute, the sample dataset changed as showed in Table 2.…”

Section: A Attribute Suppressionmentioning

confidence: 99%

Data Anonymization Techniques for Preserving Privacy in Public Release Data Model A Technical Review

Rajan,

Rajan

2020

IJSRCSE

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The protection of sensitive records is very necessary for a modern scenario. Lately, the informational index is accessible for open use for statistical analysis. In this situation increasingly sensitive information like medical records, nation resident's data, worker's compensation data and so on are affecting to a higher extent since we are giving our data to people in general. Thus, Data anonymization assumes significance in the present day to protect the open discharge of sensitive information. In this paper, we reviewed some anonymization techniques and proposed a simple anonymization technique which is the combination of synthetic data generation and pseudonymization approach which reduces attacks on sensitive facts.

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Section: A Attribute Suppressionmentioning

confidence: 99%

Data Anonymization Techniques for Preserving Privacy in Public Release Data Model A Technical Review

Rajan,

Rajan

2020

IJSRCSE

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“…The result of above methods shows that these operation causes a considerable amount of information loss because higher the generalization hierarchy more information loss will be there [1]. Also, suppression causes the elimination of values.…”

Section: B Suppressionmentioning

confidence: 99%

“…The main goal of this paper is to share data in a way that privacy is preserved while information loss is kept at least. Data that include Government agencies, University details and Medical history etc., are very necessary for an organization to do analysis and predict trends and patterns, but it may prevent the data owner from sharing the data because of privacy regulations [1]. By doing an analysis of several algorithms of Anonymization such as k-anonymity, l-diversity and tcloseness, one can achieve privacy at minimum loss.…”

mentioning

confidence: 99%

Review of Different Privacy Preserving Techniques in PPDP

Vasa¹,

Cspit²,

Modi³

2018

IJETT

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Big data is a term used for a very large data sets that have many difficulties in storing and processing the data. Analysis this much amount of data will lead to information loss. The main goal of this paper is to share data in a way that privacy is preserved while information loss is kept at least. Data that include Government agencies, University details and Medical history etc., are very necessary for an organization to do analysis and predict trends and patterns, but it may prevent the data owner from sharing the data because of privacy regulations [1]. By doing an analysis of several algorithms of Anonymization such as k-anonymity, l-diversity and tcloseness, one can achieve privacy at minimum loss. Admitting these techniques has some limitations. We need to maintain trade-off between privacy and information loss. We introduce a novel approach called Differential Privacy.

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Mining out underlying knowledge from anonymity through collaborative efforts of data miners

Kumar

2016

2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)

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Correlation Based Anonymization Using Generalization and Suppression for Disclosure Problems

Cited by 4 publications

References 18 publications

Data Anonymization Techniques for Preserving Privacy in Public Release Data Model A Technical Review

Data Anonymization Techniques for Preserving Privacy in Public Release Data Model A Technical Review

Review of Different Privacy Preserving Techniques in PPDP

Mining out underlying knowledge from anonymity through collaborative efforts of data miners

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