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.
E. Damiani—This chapter was written while Ernesto Damiani was on leave from Dipartimento di Informatica, Università degli Studi di Milano, Italia.
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Notes
- 1.
Here, the term “pipeline” is used loosely to designate any computation involving all or some of these stages, regardless of their order.
- 2.
In this case, S could be obtained by sampling DS. For the sake of simplicity, we shall assume \(S=DS\) in the remainder of this Section. So, the “estimate” is in fact the real value.
- 3.
If \(S \subsetneq DS\), i.e. a sample of customers is considered for computing the average, we expect a sampling error of the order \(O(1/{\root \of {n})}\). The Laplace random noise we have introduced has standard deviation O(1 / n), which is lower than the sampling error.
- 4.
The interested reader is referred to Michael Nielsen’s excellent online book (http://neuralnetworksanddeeplearning.com/).
- 5.
Again, adding regularization terms implicitly transforms training \(F_w\) into training a different \(F'_{w'}\), hopefully less prone to local minima.
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Acknowledgements
This work was supported by H2020 EU-funded project EVOTION (grant agreement n. H2020-727521).
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Cimato, S., Damiani, E. (2018). Some Ideas on Privacy-Aware Data Analytics in the Internet-of-Everything. In: Samarati, P., Ray, I., Ray, I. (eds) From Database to Cyber Security. Lecture Notes in Computer Science(), vol 11170. Springer, Cham. https://doi.org/10.1007/978-3-030-04834-1_6
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