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

Further- more it will present the different views according to which the right to be forgotten means the deletion of data in due time or the right to a clean slate, before pointing

• even taking into account the right to be forgotten as it is defined in the GDPR, the decision does not seem to be applying it, because under Article 17 of the GDPR the data

Another field that requires more research is the intersection between security (and especially privacy related) research and ML - be it in the form of privacy aware machine

In this paper, it applys Gaussian loss function instead of ε -insensitive loss function in a standard SVRM to devise a new model and a new type of support vector

Performing the density estimation using all the train- ing triplets would be very costly (especially on KBs with hundreds of thousand or millions of entities); for that rea- son, for

Like the face recognition and the natural language processing examples, most works discussing multi-task learning (Caruana 1997) construct ad-hoc combinations justified by a

Conformance checking in the privacy-aware process miner kit shall check for the conformance of the event log with process model, taking into consideration the relevant security

Then two distributed structure learning algorithm are illustrated: SEM- PRE (“distributed Structure lEarning by MaPREduce”) and LEAP MR (“LEArning Probabilistic description logics