Evaluating Network Neutrality requires comparing the quality of service experienced by multiple users served by different Internet Service Providers. Consequently, the issue of guaranteeing privacy-friendly network measurements has recently gained increasing interest. In this paper we propose a system which gathers throughput measurements from users of various applications and Internet services and stores it in a crowdsourced database, which can be queried by the users themselves to verify if their submitted measurements are compliant with the hypothesis of a neutral network. Since the crowdsourced data may disclose sensitive information about users and their habits, thus leading to potential privacy leakages, we adopt a privacy-preserving method based on randomized sampling and suppression of small clusters. Numerical results show that the proposed solution ensures a good trade-off between usefulness of the system, in terms of precision and recall of discriminated users, and privacy, in terms of differential privacy.
|Titolo:||Secure and Differentially Private Detection of Net Neutrality Violations by Means of Crowdsourced Measurements|
|Data di pubblicazione:||2019|
|Digital Object Identifier (DOI):||10.1007/s11277-018-5974-0|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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