Generating Partially Synthetic Geocoded Public Use Data with Decreased Disclosure Risk by Using Differential Smoothing

Quick, Harrison; Holan, Scott H.; Wikle, Christopher K.

doi:10.1111/rssa.12360

Cited by 12 publications

(9 citation statements)

References 29 publications

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“…For some applications, such as Hu et al (2014) where fully synthetic individual records were generated, the attribute disclosure risks are in the form of guessing correctly of the attributes of a record. In other applications, for example when generating synthetic geolocation applications, researchers had created attribute disclosure risk measures based on distance between synthesized geolocations and actual geolocations (Wang and Reiter, 2012;Paiva et al, 2014;Quick et al, 2015Quick et al, , 2018. Moreover, for synthetic business establishment data applications, researchers had created attribute disclosure risk measures based on percentages of closest match and their variations (Domingo-Ferrer et al, 2001;Kim et al, 2015), and other measures based on relative difference between the true largest value and the intruder's estimate (Kim et al, 2018).…”

Section: Overview Of Synthetic Data Risks Evaluationmentioning

confidence: 99%

Bayesian Estimation of Attribute and Identification Disclosure Risks in Synthetic Data

2018

Preprint

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The synthetic data approach to data confidentiality has been actively researched on, and for the past decade or so, a good number of high quality work on developing innovative synthesizers, creating appropriate utility measures and risk measures, among others, have been published. Comparing to a large volume of work on synthesizers development and utility measures creation, measuring risks has overall received less attention. This paper focuses on the detailed construction of some Bayesian methods proposed for estimating disclosure risks in synthetic data. In the processes of presenting attribute and identification disclosure risks evaluation methods, we highlight key steps, emphasize Bayesian thinking, illustrate with real application examples, and discuss challenges and future research directions. We hope to give the readers a comprehensive view of the Bayesian estimation procedures, enable synthetic data researchers and producers to use these procedures to evaluate disclosure risks, and encourage more researchers to work in this important growing field.

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Section: Overview Of Synthetic Data Risks Evaluationmentioning

confidence: 99%

Bayesian Estimation of Attribute and Identification Disclosure Risks in Synthetic Data

2018

Preprint

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“…This data is generated by reproducing the original data's statistical properties [73] and this solution become an appealing alternative when there's an issue in the availability of representative data [74]. Nevertheless, the adopted approach is to generate a partially [75] synthetic data set comprising two phases :…”

Section: Figure 10: Portion Of the Datasetmentioning

confidence: 99%

Decision Making in the Connected Learning Environment (CLE)

Belahcen¹,

Abik²,

Ajhoun³

2016

EAI Endorsed Transactions on e-Learning

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In the last years, we have witnessed to an increasingly heightened awareness of the potential benefits of a challenging and promising educational research area : Adaptive Learning [1]. It has become one of the central technologies in education [2] and was recently named, by Gartner, as the number one strategic technology to impact education in 2015 [3]. In fact, adaptive learning systems become more accessible to educational institutions, corporations, and individuals, however, the challenges encountered are more structural and operational rather than technological [4]. While a lot of research has focused on development and evaluation of technological aspects [5], serious questions remain about the motivation of learners [6],[7] and also the design of the content (or domain) model [8],[9] including the learner's autonomy issues [9],[10],[11] and the lack of the learner's control [9],[12],[13]. In order to overcome those challenges, we propose CLE "Connected Learning Environment" which is an ubiquitous learning environment [14] that provide to the learners of this generation a learning environment adapted to their expectations and their lifestyle habits and stimulate also their motivation. As a pedagogical approach, CLE adopts the connectivism [15] and take advantage from its benefits (adaptation to the current technological advances [16], management of learning in communities [17], openness with respect to external resources[18], etc.) and adapts this approach in a formal context even though the connectivism was conceived as an informal pedagogical approach [19][20]. CLE introduces a new pedagogical process including four phases detailed later (Knowledge construction, Decision making, Validation, Evaluation) and the knowledge construction phase is characterized by the collaboration and communication between heterogeneous communities composed of humans and smart objects [14]. However, the ability to distinguish relevant information among the knowledge constructed by the actors is a vital point. As part of this article, we focus on the decision making process. To do this, a comparison is made between C4.5 [21] decision tree and MLP [22] neural network on the same data set using the same performance measures in order to take a decision on the relevance of knowledge constructed by the CLE actors.

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“…While the risk of disclosure associated with the release of synthetic data is an active area of research (e.g. Hu, 2019; Quick et al., 2018; Reiter & Mitra, 2009), the drawback of many of these methods is the lack of formal privacy guarantees; for example, those implied by the use of a mechanism, p ( z | y , ψ ), that satisfies the requirements of differential privacy (Dwork et al., 2006).…”

Section: Introductionmentioning

confidence: 99%