Big Data and Analytics in the Age of the GDPR

Abstract

The new European General Data Protection Regulation places stringent restrictions on the processing of personally identifiable data. The GDPR does not only affect European companies, as the regulation applies to all the organizations that track or provide services to European citizens. Free exploratory data analysis is permitted only on anonymous data, at the cost of some legal risks. We argue that for the other kinds of personal data processing, the most flexible and safe legal basis is explicit consent. We illustrate the approach to consent management and compliance with the GDPR being developed by the European H2020 project SPECIAL, and highlight some related big data aspects.

Personal Data as a Market Commodity in the GDPR Era: A Systematic Review of Social and Economic Aspects

“…A good example to demonstrate the application of data processing technologies in this context is the retail sector. This sector has been using data processing technologies and data acquired with BDA as a tool that enables predictions about what, where, how, when and in what quantities consumers want to buy (Bonatti and Kirrane, 2019). For instance, BDA is also used by retailers to regulate the prices of their goods according to current demand and inventory.…”

“…For instance, BDA is also used by retailers to regulate the prices of their goods according to current demand and inventory. In the retail industry, as in many others, data processing technologies assist companies to adjust their online advertising to the preferences and demands of potential customers (Bonatti and Kirrane, 2019). The US retail chain Stage Stores uses big personal data for what is known as "markdown optimization, which tells merchants the best time to cut the price of a particular item in a particular store" (Bonatti and Kirrane, 2019).…”

Intelligent Software Web Agents: A Gap Analysis

“…There are also several standardisation initiatives relating to semantic web services that use formal ontology-based annotations to describe the service in a manner that can be automatically interpreted by machines. For instance, the Web Ontology Language for Web Services (OWL-S) 9 , the Web Service Modeling Language (WSML) 10 , the W3C standard Semantic Annotations for WSDL and XML Schema (SAWSDL) 11 .…”

“…Besides access control, security based research has primarily focused on applying encryption algorithms [30,39,41,57] and digital signatures [58] to RDF data. Work on privacy primarily focuses on applying and extending existing anonymisation techniques such that they work with graph data [47,63,83,93] or catering for the specification and enforcement of privacy preferences [11,88]. When it comes to trust, Artz and Gil [3] conducted a survey of existing trust mechanisms in computer science in general, and the Semantic Web in particular.…”

Distributed Skin Lesion Analysis Across Decentralised Data Sources

“…Personal data can be collected, stored, and analysed according to the legal bases defined in Article 6 of the GDPR [4]. Although GDPR does not limit the use of personal data for analytics, large-scale data collection entails the single point of failure and offers limited transparency and provenance [5]. This will lead to the loss of data sovereignty as well as the difficulty in complying with the GDPR.…”