 Various techniques exist for searching through encrypted data (Song et al. 2000, Wang et al. 2016), which provides a form of privacy protection (the data is encrypted) and selective access to sensitive data. One relatively new technique that can be used for designing privacy-preserving systems is ‘homomorphic encryption’ (Gentry 2009, Acar et al. 2018). Homomorphic encryption allows a data processor to process encrypted data, i.e. users could send personal data in encrypted form and get back some useful results – for example, recommendations of movies that online friends like – in encrypted form. The original user can then again decrypt the result and use it without revealing any personal data to the data processor. Homomorphic encryption, for example, could be used to aggregate encrypted data thereby allowing both privacy protection and useful (anonymized) aggregate information. The technique is currently not widely applied; there are serious performance issues if one wants to apply full homomorphic encryption to the large amounts of data stored in today’s systems. However, variants of the original homomorphic encryption scheme are emerging, such as Somewhat Homomorphic Encryption (Badawi et al. 2018), that are showing promise to be more widely applied in practice.