This paper presents Private Secure Routine (PSR) as a paradigm with two main objectives: i) identify drivers depending on their habits/routine and ii) keep private drivers' data. We implemented PSR exploiting the secure Multi-Party Computation (MPC) technique against a honest-but-curious attacker model. Moreover, we evaluated PSR by establishing its accuracy in combination with other existing research works based on machine learning techniques. Evaluation of PSR is performed on different test-beds, considering single-owner and two-owners identification.

Private Drivers Identification based on users' routine

G Costantino;I Matteucci;
2021

Abstract

This paper presents Private Secure Routine (PSR) as a paradigm with two main objectives: i) identify drivers depending on their habits/routine and ii) keep private drivers' data. We implemented PSR exploiting the secure Multi-Party Computation (MPC) technique against a honest-but-curious attacker model. Moreover, we evaluated PSR by establishing its accuracy in combination with other existing research works based on machine learning techniques. Evaluation of PSR is performed on different test-beds, considering single-owner and two-owners identification.
2021
Istituto di informatica e telematica - IIT
978-1-6654-3574-1
Driver identification
Privacy
Machine learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/452700
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