Lawmakers and regulatory bodies around the world are asserting Network Neutrality as a fundamental property of broadband Internet access. Since neutrality implies a comparison between different users and different ISPs, this opens the question of how to measure net neutrality in a privacy-friendly manner. This work describes a system in which users convey throughput measurements for the different services they use to a crowd-sourced database and submit queries testing their measurements against the hypothesis of a neutral network. The usage of crowd sourced databases poses potential privacy problems, because users submit data that may possibly disclose information about their own habits. This leaves the door open to information leakages regarding the content of the measurement database. Randomized sampling and suppression of small clusters can provide a good tradeoff between usefulness of the system, in terms of precision and recall of discriminated users, and privacy, in terms of differential privacy.

Differentially private queries in crowdsourced databases for net neutrality violations detection / Abba Legnazzi, M. S.; Rottondi, C.; Verticale, G.. - ELETTRONICO. - (2017), pp. 868-873. (Intervento presentato al convegno 9th International Conference on Ubiquitous and Future Networks, ICUFN 2017 tenutosi a Milan (Italy) nel 4-7 July 2017) [10.1109/ICUFN.2017.7993924].

Differentially private queries in crowdsourced databases for net neutrality violations detection

Rottondi, C.;
2017

Abstract

Lawmakers and regulatory bodies around the world are asserting Network Neutrality as a fundamental property of broadband Internet access. Since neutrality implies a comparison between different users and different ISPs, this opens the question of how to measure net neutrality in a privacy-friendly manner. This work describes a system in which users convey throughput measurements for the different services they use to a crowd-sourced database and submit queries testing their measurements against the hypothesis of a neutral network. The usage of crowd sourced databases poses potential privacy problems, because users submit data that may possibly disclose information about their own habits. This leaves the door open to information leakages regarding the content of the measurement database. Randomized sampling and suppression of small clusters can provide a good tradeoff between usefulness of the system, in terms of precision and recall of discriminated users, and privacy, in terms of differential privacy.
2017
978-1-5090-4749-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2723334
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