How sensitive is your personal information?

Al-Fedaghi, Sabah

doi:10.1145/1244002.1244046

Cited by 25 publications

(21 citation statements)

References 3 publications

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“…Discussions that move beyond the point at which personal data is somehow shared and gathered may also look to the arrangements whereby the data is stored and how provision is made for its management and retrieval [14][43] [67]. This is where concerns about security may also often kick in [3][4] [8]. Implicit in discussions about storage are concerns about the mechanisms whereby personal data is collected in the first place and then moved to a point of storage and access.…”

Section: The Notion Of Personal Data Itselfmentioning

confidence: 99%

“…A consideration underlying all of these perspectives that has long been generative of debate is the question of what might count as being 'personal' in the first place. Often there are attempts to treat what might be considered as being personal or sensitive as something relatively fixed [1] [3], and there are a number of taxonomies and ontologies to be found in this regard [65]. But there are others who seek to challenge this view, arguing that potential sensitivity is often not considered at all [28] [50] or is situationally-bound [46][54] [69].…”

Section: The Notion Of Personal Data Itselfmentioning

confidence: 99%

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The practical politics of sharing personal data

Tolmie

Crabtree

2017

Pers Ubiquit Comput

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The focus of this paper is upon how people handle the sharing of personal data as an interactional concern. A number of ethnographic studies of domestic environments are drawn upon in order to articulate a range of circumstances under which data may be shared. In particular a distinction is made between the in situ sharing of data with others around you and the sharing of data with remote parties online. A distinction is also drawn between circumstances of purposefully sharing data in some way and circumstances where the sharing of data is incidental or even unwitting. On the basis of these studies a number of the organisational features of how people seek to manage the ways in which their data is shared are teased out. The paper then reflects upon how data sharing practices have evolved to handle the increasing presence of digital systems in people's environments and how these relate to the ways in which people traditionally orient to the sharing of information. In conclusion a number of ways are pointed out in which the sharing of data remains problematic and there is a discussion of how systems may need to adapt to better support people's data sharing practices in the future.

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Section: The Notion Of Personal Data Itselfmentioning

confidence: 99%

Section: The Notion Of Personal Data Itselfmentioning

confidence: 99%

The practical politics of sharing personal data

Tolmie

Crabtree

2017

Pers Ubiquit Comput

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“…To provide opportunities for recasting and comparing the proposed approach with current approaches, then gives the new foundation by reviewing notions that have been introduced over several years in many publications, including, (AlFedaghi, 2006a;Al-Fedaghi, 2008;Al-Fedaghi, 2009;Al-Fedaghi, 2007a;Al-Fedaghi, 2007b;Al-Fedaghi, 2007c;Al-Fedaghi, 2007d;Al-Fedaghi, 2007e;AlFedaghi, 2007f;Al-Fedaghi, 2006b;Al-Fedaghi, 2006c;Al-Fedaghi, 2006d;Al-Fedaghi, 2005;AlFedaghi and Ahmad, 2006;Al-Fedaghi and AlHaqan, 2009;Al-Fedaghi and Al-Turjman, 2007;AlFedaghi and Jeragh, 2011;Al-Fedaghi and Taha, 2006;Al-Fedaghi and Thalheim, 2008;Kangassalo, 1999;Zailani and Norjihan, 2009;Sato et al, 2009). Some materials are presented in new ways.…”

Section: Issue Of Analysismentioning

confidence: 99%

Engineering Privacy Revisited

Al-Fedaghi¹

2012

Journal of Computer Science

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Problem statement: Information Privacy Engineering (IPE) is the field that studies the protection of privacy in information and communication systems. The theoretical, technological and applications aspects of IPE require a framework that provides a general view and a systematic structure for the discipline's topics. This study discusses certain characteristics of such a framework and proposes enhancing and strengthening its structure. Approach: Several important problems and their solutions are presented through recasting of some current proposed approaches to Personal Identifiable Information (PII) definitions and handling. Results: A new framework is presented that is based on flow-based model, along with generic operations performed on PII. Conclusion: This study shows that the flow-based model can provide a structure that complements current efforts to develop a framework for IPE.

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“…Personal information refers to individually identifiable information. However, personal information does not mean the information is especially sensitive, private or embarrassing [13]. We propose to consider the sensitivity of private information in the data publishing models.…”

Section: A Sensitivity Of Private Informationmentioning

confidence: 99%

A new perspective of privacy protection: Unique distinct l-SR diversity

Wang

Yan

Luo

et al. 2010

2010 Eighth International Conference on Privacy, Security and Trust

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Abstract-More and more public data sets which contain information about individuals are published in recent years. The urgency to reduce the risk of the privacy disclosure from such data sets makes the approaches of privacy protection for data publishing be widely employed. There are two popular models for privacy protection: k-anonymity and l-diversity. kanonymity focuses on reducing the probability of identifying a particular person, which requires that each equivalence class (a set of records with same identifier attributes) contains at least k records. l-diversity concentrates on reducing the inference from released sensitive attributes. It requires that each equivalence class has at least l "well-represented" sensitive attribute values. In this study, we view the privacy protection problem in a brand new perspective. We proposed a new model, Unique Distinct l-SR diversity based on the sensitivity of private information. Also, we presented two performance measures for how much sensitive information can be inferred from an equivalence class. l-SR diversity algorithm was implemented to achieve Unique Distinct l-SR diversity. We tested l-SR diversity on one benchmark data set and three synthetic data sets, and compared it with other l-diversity algorithms. The results show that our algorithm achieved better performance on minimizing inference of sensitive information and reached the comparable generalization data quality compared with other data publishing algorithms.

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How sensitive is your personal information?

Cited by 25 publications

References 3 publications

The practical politics of sharing personal data

The practical politics of sharing personal data

Engineering Privacy Revisited

A new perspective of privacy protection: Unique distinct l-SR diversity

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