Title: Utilizing CPU, Memory and other features signals to control processes and related data in computing devices with potential to identify user. An Application Risk Assessment Approach
Authors: GENEIATAKIS DIMITRIOSMALATRAS APOSTOLOSVAKALIS Ioannis
Publisher: Publications Office of the European Union
Publication Year: 2014
JRC N°: JRC93611
ISBN: 978-92-79-45025-9 (print)
978-92-79-45027-3 (pdf)
978-92-79-45026-6 (CD-ROM)
ISSN: 1018-5593 (print)
1831-9424 (online)
1831-9424 (CD-ROM)
Other Identifiers: EUR 27054
OP LB-NA-27054-EN-C (print)
OP LB-NA-27054-EN-N (online)
OP LB-NA-27054-EN-Z (CD-ROM)
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC93611
DOI: 10.2788/812992
10.2788/259848
10.2788/54365
Type: EUR - Scientific and Technical Research Reports
Abstract: In the Internet era users' fundamental privacy and anonymity rights have received significant research and regulatory attention. This is not only a result of the exponential growth of data that users generate when accomplishing their daily task by means of computing devices with advanced capabilities, but also because of inherent data properties that allow them to be linked with a real or soft identity. Service providers exploit these facts for user monitoring and identification, albeit impacting users' anonymity, based mainly on personal identifiable information or on sensors that generate unique data to provide personalized services. In this paper, we report on the feasibility of user identification using instead general system features like memory, CPU and network data, as provided by the underlying operating system. We provide a general framework based on supervised machine learning algorithms both for distinguishing users, and informing them about their anonymity exposure. We conduct a series of experiments to collect trial datasets for users' engagement on a shared computing platform. We evaluate various well-known classifiers in terms of their effectiveness in distinguishing users, and we perform a sensitivity analysis of their configuration setup to discover optimal settings under diverse conditions. Furthermore, we examine the bounds of sampling data to eliminate the chances of user identification and thus promote anonymity. Overall results show that under certain configurations users' anonymity can be preserved, while in other cases users' identification can be inferred with high accuracy, without relying on personal identifiable information.
JRC Directorate:Space, Security and Migration

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