Approved
Anonymously Publishing Univariate Time-Series: With focus on (k,P)-Anonymity
Erik Wik (2014)
Start
2019-01-21
Presentation
2019-12-19 13:15
Location:
E:2349
Finished:
2020-01-20
Master's thesis:
Abstract
Anonymizing time-series data is a demanding task since there might be a lot of information which can be inferred which might not initially be obvious. Therefore, it can be desired to anonymize with some established privacy guarantee. Furthermore, there is always a trade-off to be made between privacy and minimization of information loss. In this thesis, an investigation is made into how to protect univariate time-series. The main focus of is on anonymizing time-series from individual users, but methods for anonymizing aggregate time-series and the removal of sensitive data is also investigated, in order to find a wider understanding of how a database can be anonymized. The main achievement of this thesis is the implementation of PC-KAPRA, a novel extension to the KAPRA algorithm, which publishes data under (k,P)-anonymity. It is shown that PC-KAPRA offers a large improvement in retaining pattern information compared to KAPRA, and publishes data with qualitative useful information.
Supervisor: Peter Blomqvist (Sony Mobile Communications) and Christian Gehrmann (EIT)
Examiner: Thomas Johansson (EIT)