The Right to Be Forgotten: Towards Machine Learning on Perturbed Knowledge Bases

Abstract

Part 2: Special Session on Privacy Aware Machine Learning for Health Data Science (PAML 2016)International audienceToday’s increasingly complex information infrastructures represent the basis of any data-driven industries which are rapidly becoming the 21st century’s economic backbone. The sensitivity of those infrastructures to disturbances in their knowledge bases is therefore of crucial interest for companies, organizations, customers and regulating bodies. This holds true with respect to the direct provisioning of such information in crucial applications like clinical settings or the energy industry, but also when considering additional insights, predictions and personalized services that are enabled by the automatic processing of those data. In the light of new EU Data Protection regulations applying from 2018 onwards which give customers the right to have their data deleted on request, information processing bodies will have to react to these changing jurisdictional (and therefore economic) conditions. Their choices include a re-design of their data infrastructure as well as preventive actions like anonymization of databases per default. Therefore, insights into the effects of perturbed/anonymized knowledge bases on the quality of machine learning results are a crucial basis for successfully facing those future challenges. In this paper we introduce a series of experiments we conducted on applying four different classifiers to an established dataset, as well as several distorted versions of it and present our initial results

What makes a mobile app successful in supporting health behaviour change?

“…Mental health app data infrastructure must therefore be constructed in such a way so as to reduce the risk of data becoming identifiable with the user and or necessitating a user request for deletion (Malle et al, 2016). The aim is to reduce the identifiable content of a data piece from the specific to the general (Malle et al, 2016). Personal identifiers (e.g.…”

“…Security issues relating to the content, holding and distribution of that data however remain a prime concern of users and health care professionals (Marley and Farooq, 2015;Malle et al, 2016). Data protection regulations also known as 'The right to be forgotten' means customers can have their data deleted on request (Malle et al, 2016).…”

Introduction to MAchine Learning & Knowledge Extraction (MAKE)

“…Incorporating the social embedding of nodes into our model would make that draw depend on the values of connected nodes in the graph, allowing us to apply efficient sampling methods such as MCMC (Markov Chain Monte Carlo) [81] to the problem. As a result, ML performance on anonymized graphs could be boosted without any personal re-identification attempts; a crucial advantage as more and more countries adopt stringent data privacy and security laws [82].…”

Machine Learning for Health Informatics

“…Privacy aware machine learning and privacy preserving machine learning is an important issue [98,99], fostered by anonymization concepts, in which a record is released only if it is indistinguishable from k other entities in the data. k-anonymity is highly dependent on spatial locality in order to effectively implement the technique in a statistically robust way and in high dimensions data becomes sparse, hence the concept of spatial locality is not easy to define.…”