Some ideas on privacy-aware data analytics in the internet-of-everything

Stelvio Cimato, Ernesto Damiani

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

In this chapter, we discuss some issues concerning the computation of machine learning models for data analytics on the Internet-of-Everything. We model such computations as compositions of services that form a process whose main stages are acquisition, preparation, model training, and model-based inference. Then, we discuss randomiza-tion-as-a-service as a key technique for limiting undesired information disclosure during this process. We recall some fundamental results showing that randomization decreases the severity of disclosure, but at the same time has an adverse effect on data utility, in our case the data business value within the specific IoE application. We argue that non-interactive randomization at data acquisition time, while decreasing utility, can provide maximum flexibility and best accommodate provisions for compliance with regulations, ethics and cultural factors.

Original languageBritish English
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages113-124
Number of pages12
DOIs
StatePublished - 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11170 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Ethics
  • Internet-of-everything
  • Machine learning models
  • Privacy

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