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Privacy by design in big data
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Privacy by design in big data

An overview of privacy enhancing technologies in the era of big data analytics

Privacy by design in big data

An overview of privacy enhancing technologies in the era of big data analytics

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The extensive collection and further processing of personal information in the context of big data analytics has given rise to serious privacy concerns, especially relating to wide scale electronic surveillance, profiling, and disclosure of private data. In order to allow for all the benefits of analytics without invading individuals’ private sphere, it is of utmost importance to draw the limits of big data processing and integrate the appropriate data protection safeguards in the core of the analytics value chain. ENISA, with the current report, aims at supporting this approach, taking the position that, with respect to the underlying legal obligations, the challenges of technology (for big data) should be addressed by the opportunities of technology (for privacy). To this end, in the present study we first explain the need to shift the discussion from “big data versus privacy” to “big data with privacy”, adopting the privacy and data protection principles as an essential value of big data, not only for the benefit of the individuals, but also for the very prosperity of big data analytics. In this respect, the concept of privacy by design is key in identifying the privacy requirements early at the big data analytics value chain and in subsequently implementing the necessary technical and organizational measures. Therefore, after an analysis of the proposed privacy by design strategies in the different phases of the big data value chain, we provide an overview of specific identified privacy enhancing technologies that we find of special interest for the current and future big data landscape. In particular, we discuss anonymization, the “traditional” analytics technique, the emerging area of encrypted search and privacy preserving computations, granular access control mechanisms, policy enforcement and accountability, as well as data provenance issues. Moreover, new transparency and access tools in big data are explored, together with techniques for user empowerment and control. Following the aforementioned work, one immediate conclusion that can be derived is that achieving “big data with privacy” is not an easy task and a lot of research and implementation is still needed. Yet, we find that this task can be possible, as long as all the involved stakeholders take the necessary steps to integrate privacy and data protection safeguards in the heart of big data, by design and by default.
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