A Framework for Privacy-Preserving White-Box Anomaly Detection using a Lattice-Based Access Control

Abstract

Privacy concerns are amongst the core issues that will constrain the adoption of distributed anomaly detection. Indeed, when outsourcing anomaly detection, i.e. with a party other than the data owner running the detection, confidential or private aspects of the observed data may need protection. Some privacy-enhancing function is usually employed. Because of the impact that this restriction causes in the creation of explainable alerts, finding mechanisms to balance the trade-off between privacy and usefulness has become increasingly important. Due to this motivation, in this paper, a privacy-preserving white-box anomaly detection framework is presented to facilitate matching the compatibility between service requirements and privacy restrictions of an user by using an access control based on a lattice of privacy protection levels. Our framework allows entities to verify these trade-offs by specifying required protection at the level of features. We evaluate the framework in a real-world scenario within the e-health setting. The results point out that it can generate interpretable alerts while protecting the confidentiality of the data.