On the Question of Statistical Confidentiality

Fellegi, Ivan P.

doi:10.1080/01621459.1972.10481199

Cited by 124 publications

(36 citation statements)

References 2 publications

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“…Disclosure in the context of this report is understood to be the identification of a sample record in a released dataset that has occurred without any prior knowledge of the survey respondent being part of the sampled dataset and the recognition of additional attributes of the survey respondent (Fellegi, 1972). Identity and attribute disclosure risk were discussed in the literature review and are reflected in the three-tiered hierarchical framework for evaluating confidentiality disclosure illustrated in Figure 3-1 (Clifton and Noyan, 2012).…”

Section: Evaluation Of Disclosure Riskmentioning

confidence: 99%

Wider Dissemination of Household Travel Survey Data Using Geographical Perturbation Methods

Clifton¹,

Gehrke²

2014

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Section: Evaluation Of Disclosure Riskmentioning

confidence: 99%

Wider Dissemination of Household Travel Survey Data Using Geographical Perturbation Methods

Clifton¹,

Gehrke²

2014

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“…The term 'confidentiality' refers to preventing disclosure of information to unauthorised parties. Fellegi (1972) characterised 'inadvertent direct disclosure' as depending on two elements: firstly, that an individual must be identifiable in the released data (identity disclosure) and secondly the released data must reveal information further to that which was used in the identification process (attribute disclosure). For statistical agencies, being seen to ensure confidentiality may be an important element of building public trust.…”

Section: Confidentialitymentioning

confidence: 99%

Location Tracing and Potential Risks in Interaction Data Sets

Duke‐Williams¹

2017

Appl. Spatial Analysis

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Location-aware mobile phone handsets have become increasingly common in recent years, giving rise to a wide variety of location based services that rely on a person's mobile phone reporting its current location to a remote service provider. Previous research has demonstrated that services that geo-code status updates may permit the estimation of both the rough location of users' home locations and those of their workplaces. The paper investigates the disclosure risks of a priori knowledge of a person's home and workplace locations, or of their current and previous home locations. Detailed interaction data sets published from censuses or other sources are characterised by the sparsity of the contained data, such that unique combinations of two locations may often be observed. In the most detailed 2011 migration data 37% of migrants had a unique combination of origin and destination, whilst in the most detailed journey to work data, 58% of workers had a unique combination of home and workplace. The amount of additional attribute data that might be disclosed is limited. When more coarse geographies are used their still remain a non-trivial number of persons with unique location combinations, with considerably more attributes potentially disclosable.

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“…Micro-data disclosure has been extensively studied [1,3,10,16,17] where the security issue discussed in this paper is largely ignored. In particular, data swapping [9,23,28] and cell suppression [18] both aim to protect micro-data released in census tables.…”

Section: Related Workmentioning

confidence: 99%

Exclusive Strategy for Generalization Algorithms in Micro-data Disclosure

Zhang

Wang

Jajodia

et al. 2008

Lecture Notes in Computer Science

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Abstract. When generalization algorithms are known to the public, an adversary can obtain a more precise estimation of the secret table than what can be deduced from the disclosed generalization result. Therefore, whether a generalization algorithm can satisfy a privacy property should be judged based on such an estimation. In this paper, we show that the computation of the estimation is inherently a recursive process that exhibits a high complexity when generalization algorithms take a straightforward inclusive strategy. To facilitate the design of more efficient generalization algorithms, we suggest an alternative exclusive strategy, which adopts a seemingly drastic approach to eliminate the need for recursion. Surprisingly, the data utility of the two strategies are actually not comparable and the exclusive strategy can provide better data utility in certain cases.

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On the Question of Statistical Confidentiality

Cited by 124 publications

References 2 publications

Wider Dissemination of Household Travel Survey Data Using Geographical Perturbation Methods

Wider Dissemination of Household Travel Survey Data Using Geographical Perturbation Methods

Location Tracing and Potential Risks in Interaction Data Sets

Exclusive Strategy for Generalization Algorithms in Micro-data Disclosure

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