Smart meter data can be identifying and revealing - more so than many people appreciate. For example, someone may volunteer to share their energy data, but does not want to reveal their personal religious practices. In some cases it may be possible to infer such information, when energy demand patterns differ during Ramadan or on other religious occasions.
To make an informed decision for consent, one needs to understand what information can and cannot be revealed and what measures can be put in place to safeguard against undesirable disclosure. This applies to both the person providing and the organisation processing the data.
In this lab, consenting participants will submit detailed personal information and we train models to identify which of these features can be detected through sophisticated analysis. Subsequently we apply methods to obfuscate, aggregate and synthesis the data until they can be pronounced ‘benign’.