Personal Data-Less Personalized Software Applications

Adoption of software solutions is often hindered by privacy concerns, especially for applications which aim to collect data capable of `total privacy eradication'. To address this, the General Data Protection Regulation (GDPR) has introduced the Data Minimization principle that stipulates on only collecting the minimum amount of data necessary to achieve a legitimate and pre-defined purpose. Privacy researchers have argued that this principle has led to a privacy-utility trade-off, claiming that the less personal data is collected by a software application the less utility users receive from that software. In this paper, we demonstrate that software can be designed to provide quite "personalized" utility even before any sensitive personal data is collected. To do so, we have re-engineered the software use process by allowing users to self-categorize within personas (i.e., generic user categories with similar software use needs to that of the intended beneficiary user groups). This approach is illustrated with a case study of home energy management system design. Only when a householder decides to fully use particular personalization features to fine-tune the application to their own needs would this householder choose to give up their personal data.