Securing the Data Economy: Translating Privacy and Enacting Security in the Development of DataSHIELD

Abstract

Contemporary bioscience is seeing the emergence of a new data economy: with data as its fundamental unit of exchange. While sharing data within this new ‘economy’ provides many potential advantages, the sharing of individual data raises important social and ethical concerns. We examine ongoing development of one technology, DataSHIELD, which appears to elide privacy concerns about sharing data by enabling shared analysis while not actually sharing any individual-level data. We combine presentation of the development of DataSHIELD with presentation of an ethnographic study of a workshop to test the technology. DataSHIELD produced an application of the norm of privacy that was practical, flexible and operationalizable in researchers’ everyday activities, and one which fulfilled the requirements of ethics committees. We demonstrated that an analysis run via DataSHIELD could precisely replicate results produced by a standard analysis where all data are physically pooled and analyzed together. In developing DataSHIELD, the ethical concept of privacy was transformed into an issue of security. Development of DataSHIELD was based on social practices as well as scientific and ethical motivations. Therefore, the ‘success’ of DataSHIELD would, likewise, be dependent on more than just the mathematics and the security of the technology.

DataSHIELD – New Directions and Dimensions

“…Following initial proof-of-principle (Wolfson et al 2010;Jones et al 2012;Gaye et al 2014;Doiron et al 2013) a stable platform has been developed (available under a GPL3 license) and the legal, ethical and social issues arising from the DataSHIELD approach to the analysis of biomedical data have been reviewed Budin-Ljøsne et al 2014;Murtagh et al 2012Murtagh et al , 2016. DataSHIELD has been successfully piloted within two epidemiological projects in the FP7-funded BioSHaRE-EU consortium 7 -co-analysing phenotypic data from separately located European biobanks investigating i) healthy obesity comprising 10 biobanks with 99 phenotypic variables and ii) the effect of environmental determinants on health comprising five biobanks with 51 phenotypic variables and 14 environmental variables extracted from exposure models Zijlema et al 2016).…”

Software for Distributed Computation on Medical Databases: A Demonstration Project

“…In many circumstances, such as in fitting models based on maximizing a likelihood, the computations can be distributed, with aggregation limited to the intermediate results of calculations on local data, rather than raw data; see Murtagh et al (2012) for example. Indeed, sometimes distribution of the calculation among sites is necessary to share a heavy computational burden, as would be the case for fitting the alternating direction method of multipliers (ADMM) models of Boyd, Parikh, Chu, Peleato, and Eckstein (2011).…”

Protecting Personal Data in Epidemiological Research: DataSHIELD and UK Law

“…[1]. These collaborations present administrative, ethical, and legal challenges as the confidentiality of the identifiable data collected from participants must be protected and its sharing controlled through applicable legislation, guidance, ethics review, and data access mechanisms [2]. While such controls rightly provide protections, there is a perception that they may also limit access to data and may hinder the research process [3].…”

“…Terms reflecting between-study heterogeneity must typically be incorporated. Individual-level data are variously referred to as ‘individual patient data' by meta-analysts and as ‘micro-data' by bioinformaticians [2]. As an alternative, combined analysis may be based on study-level meta-analysis (SLMA).…”

“…And so, under DataSHIELD, although a full individual-level analysis is enabled, no identifying or sensitive information is physically moved, or even rendered temporarily visible, outside the original study in which the data were collected [17]. In this way DataSHIELD seeks ‘… to elide privacy concerns about sharing data' while enabling a flexible interrogation of the participating datasets [2]. It is currently being piloted in several projects, including the Healthy Obese Project (HOP), under the Seventh Framework Programme for Research-funded BioSHaRE study [18].…”