Privacy and anonymization for very large datasets

Muntés-Mulero, Víctor; Nin, Jordi

doi:10.1145/1645953.1646333

Cited by 30 publications

(11 citation statements)

References 15 publications

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“…Literature [5] achieved to protect data privacy through multi-layer encryption in relational databases. Munts discussed the existing privacy protection technologies, including K anonymity, anonymous figure and data preprocessing as the massive data released [6].…”

Section: Related Workmentioning

confidence: 99%

Trust Based Service Optimization Selection for Cloud Computing

Li¹,

He²,

Du³

2015

IJMUE

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Section: Related Workmentioning

confidence: 99%

Trust Based Service Optimization Selection for Cloud Computing

Li¹,

He²,

Du³

2015

IJMUE

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“…While social network graphs can be represented in tabular form, the semantics are not the same as those for tabular data. Applying classical anonymization techniques to a tabular representation of a graph will either break the data structure or fail to provide anonymity [23].…”

Section: Anonymization Of Published Datamentioning

confidence: 99%

Social networking applications in health care

Williams

2010

Proceedings of the 2010 ICSE Workshop on Software Engineering in Health Care

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The advent of social networking websites for use in health care has prompted concerns about the risks that such systems pose to the security and privacy of personal health information. In this paper, we survey the research literature, in order to provide a current snapshot of privacy and security safeguards for social network websites. We describe some of the unique features of the health care space, and recommend directions for future research in this relatively new area.

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“…Literature [4] achieved data protection in the relational database through a multi-layer encryption. Munts [5] discussed the existing data processing technology, including K anonymous, anonymous figure and data preprocessing, as the large-scale release of data problems faced some solutions. Roy focused on information flow control and differential data protection technologies into cloud computing to generate phase data in which propose a data protection system airavat, prevent mapreduce calculation authorized private data leaked out.…”

Section: Introductionmentioning

confidence: 99%

Trust Based Service Selection in Cloud Computing Environment

Li¹,

Liang²,

Zhang³

2016

IJSH

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In order to meet cloud user demand for managed service, we propose a scheme based on trust for cloud service selection. The scheme includes the service chosen and services delivered two parts, which can choose the cloud service through its trust value, to meet the safety requirements premise, and make trust decisions to achieve service controllability. First it make choice of the service in accordance with service attributes weight using AHP, then complete user data delivery rely on trust, which allowing users to combine with their own situation and trust in cloud services dynamically adjusting the cloud user data release granularity to reach the controllability of the service .Instance results show that the scheme is effective and can guarantee service selection under safe and controlled conditions.

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Privacy and anonymization for very large datasets

Cited by 30 publications

References 15 publications

Trust Based Service Optimization Selection for Cloud Computing

Trust Based Service Optimization Selection for Cloud Computing

Social networking applications in health care

Trust Based Service Selection in Cloud Computing Environment

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