Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics

The extensive collection and processing of personal information in big data analytics has given rise to serious privacy concerns, related to wide scale electronic surveillance, profiling, and disclosure of private data. To reap the benefits of analytics without invading the individuals' private sphere, it is essential to draw the limits of big data processing and integrate data protection safeguards in the analytics value chain. ENISA, with the current report, supports this approach and the position that the challenges of technology (for big data) should be addressed by the opportunities of technology (for privacy). We first explain the need to shift from "big data versus privacy" to "big data with privacy". In this respect, the concept of privacy by design is key to identify the privacy requirements early in the big data analytics value chain and in subsequently implementing the necessary technical and organizational measures. After an analysis of the proposed privacy by design strategies in the different phases of the big data value chain, we review privacy enhancing technologies 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. Achieving "big data with privacy" is no easy task and a lot of research and implementation is still needed. Yet, it remains a possible task, 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.