A Domain Ontology and Software Platform for Collaborative Personal Data Analytics

The use of public sector data analytics in the Netherlands

Transforming Government People Process and Policy ◽

10.1108/tg-09-2019-0095 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Anne Fleur van Veenstra ◽

Francisca Grommé ◽

Somayeh Djafari

Keyword(s):

Public Sector ◽

The Netherlands ◽

Social Services ◽

Data Analytics ◽

Web Search ◽

Single Case ◽

Personal Data ◽

Comprehensive Overview ◽

Content Type ◽

Sector Data

Purpose Public sector data analytics concerns the process of retrieving data, data analysis, publication of the results as well as re-using the data by government organizations to improve their operations and enhance public policy. This paper aims to explore the use of public sector data analytics in the Netherlands and the opportunities and challenges of this use. Design/methodology/approach This paper finds 74 applications of public sector data analytics, identified by a Web search and consultation with policymakers. The applications are categorized by application type, organization(s) involved and application domain, and illustrative examples are used to elaborate opportunities and challenges. Findings Public sector data analytics is most frequently used for inspection and enforcement of social services and for criminal investigation. Even though its usage is often experimental, it raises concerns for scope creep, repeated targeting of the same (group of) individuals, personal data use by third parties and the transparency of governmental processes. Research limitations/implications Drawing on desk research, it was not always possible to identify which type of data or which technology was used in the applications that were found. Furthermore, the case studies are illustrative rather than providing an in-depth overview of opportunities and challenges of the use of data analytics in government. Originality/value Most studies either perform a literature overview or present a single case study; this paper presents a more comprehensive overview of how a public sector uses data analytics.

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Balancing Privacy Vs Efficiency in Data Analytics using Nearest Neighbour Randomization

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l2567.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 2289-2295

Keyword(s):

Data Analytics ◽

Large Scale ◽

Personal Data ◽

Privacy And Security ◽

Computing Platform ◽

Use Of Data ◽

Digital Era ◽

Considerable Research ◽

Group Data ◽

Security Practices

The Digital era marked by the unrivalled growth of Internet and its services with day-to-day technological advancements has paved way for a data driven society. This digital explosion offers opportunities for extracting valuable information from collected data, which are used by organizations and research establishments for synergistic advantage. However, privacy of online divulged data is an issue that gets overlooked as a consequence of such large-scale analytics. Although, privacy and security practices conjointly determine the ethics of data collection and its use, personal data of individuals is largely at risk of disclosure. Considerable research has gone into privacy preserving analytics, in the light of Big Data and IoT boom, but scalable and efficient techniques, that do not compromise the usefulness of privacy constrained data, continues to be a challenging arena for research. The proposed work makes use of a distance-based perturbation method to group data and further randomizes data. The efficacy of perturbed data is evaluated for classification task that gives results on par with the non-perturbed counterpart. The relative performance of the algorithm is also evaluated on the parallel computing platform Spark. Results show that the technique does not hinder the use of data for holistic analysis while privacy is subjectively maintained.

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Wearable Devices and Privacy Concerns

Research Anthology on Privatizing and Securing Data ◽

10.4018/978-1-7998-8954-0.ch009 ◽

2021 ◽

pp. 208-230

Author(s):

Ersin Dincelli ◽

Xin Zhou ◽

Alper Yayla ◽

Haadi Jafarian

Keyword(s):

Data Collection ◽

Social Networking ◽

Data Analytics ◽

Data Privacy ◽

Personal Data ◽

Wearable Devices ◽

Physical Activities ◽

Lifestyle Habits ◽

Privacy Concerns ◽

Daily Physical Activities

Wearable devices have evolved over the years and shown significant increase in popularity. With the advances in sensor technologies, data collection capabilities, and data analytics, wearable devices now enable interaction among users, devices, and their environment seamlessly. Multifunctional nature of this technology enables users to track their daily physical activities, engage with other users through social networking capabilities, and log their lifestyle habits. In this chapter, the authors discuss the types of sensor technologies embedded in wearable devices and how the data collected through such devices can be further interpreted by data analytics. In parallel with abundance of personal data that can be collected via wearable devices, they also discuss issues related to data privacy, suggestions for users, developers, and policymakers regarding how to protect data privacy are also discussed.

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The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.511 ◽

2020 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Brinnae Bent ◽

Ke Wang ◽

Emilia Grzesiak ◽

Chentian Jiang ◽

Yuankai Qi ◽

...

Keyword(s):

Health Outcomes ◽

Open Source ◽

Open Source Software ◽

Biomarker Discovery ◽

Digital Health ◽

Personal Data ◽

Software Platform ◽

Biomarker Research ◽

Digital Biomarkers ◽

Discovery Pipeline

Abstract Introduction: Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into digital biomarkers that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. There are many challenges faced by digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open-source data and code. Further, the process of transforming data into digital biomarkers is computationally expensive, and standards and validation methods in digital biomarker research are lacking. Methods: In order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open-source software platform for end-to-end digital biomarker development: The Digital Biomarker Discovery Pipeline (DBDP). Results: Here, we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases. Conclusions: The clear need for such a platform will accelerate the DBDP’s adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.

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Cloud-Based Software Platform for Big Data Analytics in Smart Grids

Computing in Science & Engineering ◽

10.1109/mcse.2013.39 ◽

2013 ◽

Vol 15 (4) ◽

pp. 38-47 ◽

Cited By ~ 176

Author(s):

Yogesh Simmhan ◽

Saima Aman ◽

Alok Kumbhare ◽

Rongyang Liu ◽

Sam Stevens ◽

...

Keyword(s):

Big Data ◽

Smart Grids ◽

Data Analytics ◽

Big Data Analytics ◽

Software Platform

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Introduction to the Minitrack on Personal Data: Analytics and Management

10.24251/hicss.2022.224 ◽

2022 ◽

Author(s):

Glenn Parry ◽

Philip Davies ◽

Joo Oh

Keyword(s):

Data Analytics ◽

Personal Data

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Political Micro-Targeting in Kenya: An Analysis of the Legality of Data-Driven Campaign Strategies under the Data Protection Act

Journal of Intellectual Property and Information Technology Law (JIPIT) ◽

10.52907/jipit.v1i1.61 ◽

2021 ◽

Vol 1 (1) ◽

pp. 7-36

Author(s):

Hashim Mude

Keyword(s):

Political Parties ◽

Data Protection ◽

Political Party ◽

Data Analytics ◽

Personal Data ◽

Data Driven ◽

Campaign Strategies ◽

Data Profiling ◽

Targeted Messages ◽

Data Subject

The 2013 general election marked the entry of data-driven campaigning into Kenyan politics as political parties begun collecting and storing voter data. More sophisticated techniques were deployed in 2017 as politicians retained the services of data analytics firms such as Cambridge Analytica, accused of digital colonialism and undermining democracies. It is alleged that political parties engaged in regular targeting and more intrusive micro-targeting, facilitated by the absence of a data protection legal framework.The promulgation of the Data Protection Act, 2019, ostensibly remedied this gap. This paper analyses whether, and to what extent, political parties can rely on the same–or similar– regular targeting and micro-targeting techniques in subsequent elections. While regular targeting differs from micro-targeting as the latter operates at a more granular level, both comprise of three steps- collecting a voter’s personal data, profiling them, and sending out targeted messages. This paper considers the legality of each of these steps in turn. It finds that going forward, such practices will likely require the consent of the data subject. However, the Act provides for several exceptions which political parties could abuse to circumvent this requirement. There are also considerable loopholes that allow open access to voter data in the electoral list as well as the personal data of the members of a rival political party. The efficacy of the Data Protection Act will largely rest on whether the Data Protection Commissioner will interpret it progressively and hold political parties to account.

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