Practical approaches to big data privacy over time

Altman, Micah; Wood, Alexandra; O’Brien, David R.; Gasser, Urs

doi:10.1093/idpl/ipx027

Cited by 71 publications

(24 citation statements)

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“…Aside from being a generally useful approach for fitting hierarchical models recursively, the Proposal-RB procedure is useful in data privacy situations where the original data cannot be released due to proprietary reasons, public safety, or legal restrictions (Altman, 2018) because the data do not appear in the second stage analysis. Proposal-RB is also trivial to implement and is naturally adapted for parallel computing environments because we can sample from each of the transient posterior distributions [β j |y j ] in parallel at the first stage.…”

Section: Proposal-recursive Bayesian Inferencementioning

confidence: 99%

Making Recursive Bayesian Inference Accessible

Hooten

Johnson

Brost

2019

The American Statistician

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Bayesian models provide recursive inference naturally because they can formally reconcile new data and existing scientific information. However, popular use of Bayesian methods often avoids priors that are based on exact posterior distributions resulting from former studies. Two existing Recursive Bayesian methods are: Prior-and Proposal-Recursive Bayes. Prior-Recursive Bayes uses Bayesian updating, fitting models to partitions of data sequentially, and provides a way to accommodate new data as they become available using the posterior from the previous stage as the prior in the new stage based on the latest data. Proposal-Recursive Bayes is intended for use with hierarchical Bayesian models and uses a set of transient priors in first stage independent analyses of the data partitions. The second stage of Proposal-Recursive Bayes uses the posteriors from the first stage as proposals in an MCMC algorithm to fit the full model. We combine Prior-and Proposal-Recursive concepts to fit any Bayesian model, and often with computational improvements. We demonstrate our method with two case studies. Our approach has implications for big data, streaming data, and optimal adaptive design situations.

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Section: Proposal-recursive Bayesian Inferencementioning

confidence: 99%

Making Recursive Bayesian Inference Accessible

Hooten

Johnson

Brost

2019

The American Statistician

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“…In their words, the availability of Smart Grids, automated building systems, unmanned aerial vehicles and sensors that are enabled by IoT and cloud platforms are all geared towards rendering a more comfortable urban life. Nevertheless, the heterogeneity of the platforms and protocols that allows for a smooth application of all these systems exposes security threats, especially to hackers, terrorists, scammers and other ill-minded groups [78]. The challenges are even more pronounced in regard to the Big Data generated by these diverse components.…”

Section: An Agenda For Urban Comfortmentioning

confidence: 99%

The Emergence of Anti-Privacy and Control at the Nexus between the Concepts of Safe City and Smart City

Allam

2019

Smart Cities

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The emergence of Big Data, accelerated through the Internet of Things (IoT) and Artificial Intelligence, from the emerging, contemporary concept of smart cities coupled with that of the notion for safe cities is raising concerns of privacy and good governance that are impacting on socio-economic and liveability dimensions of urban fabrics. As these gain ground, largely due to economic pressures from large ICT providers, there is a notable increase towards the need for inclusion of human dimensions, complemented by the use of technology. However, the latter is seen as catalysing elements of control and propaganda which are thriving through oversimplified and non-inclusive urban IT policy measures. This paper dwells on the intersecting subjects of smart and safe cities and explores the highlighted issues that are deemed to cause concern and further explore the need for transparency and inclusivity in urban processes and systems. This paper is oriented towards urban planners and policy makers looking at the implementation of smart and safe cities concepts.

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“…They proposed privacy preserving approach for data lake scenario. The privacy challenges associated with big data are discussed in [16] while different solutions for the same are found in [17].…”

Section: Related Workmentioning

confidence: 99%

One Size Does Not Fit All: Trade-offs between Misuse Probability and Level of Sanitization for Big Data

2019

IJRTE

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Big data privacy has assumed importance as the cloud computing became a phenomenal success in providing a remote platform for sharing computing resources without geographical and time restrictions. However, the privacy concerns on the big data being outsourced to public cloud storage are still exist. Different anonymity or sanitization techniques came into existence for protecting big data from privacy attacks. In our prior works, we have proposed a misusability probability based metric to know the probable percentage of misusability. We additionally planned a system that suggests level of sanitization before actually applying privacy protection to big data. It was based on misusability probability. In this paper, our focus is on further evaluation of our misuse probability based sanitization of big data approach by defining an algorithm which willanalyse the trade-offs between misuse probability and level of sanitization. It throws light into the proposed framework and misusability measure besides evaluation of the framework with an empirical study. Empirical study is made in public cloud environment with Amazon EC2 (compute engine), S3 (storage service) and EMR (MapReduce framework). The experimental results revealed the dynamics of the trade-offs between them. The insights help in making well informed decisions while sanitizing big data to ensure that it is protected without losing utility required.

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Practical approaches to big data privacy over time

Cited by 71 publications

References 0 publications

Making Recursive Bayesian Inference Accessible

Making Recursive Bayesian Inference Accessible

The Emergence of Anti-Privacy and Control at the Nexus between the Concepts of Safe City and Smart City

One Size Does Not Fit All: Trade-offs between Misuse Probability and Level of Sanitization for Big Data

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