Name
Abstract | ||
|---|---|---|
In the Internet era, usersu0027 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 usersu0027 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 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 usersu0027 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 usersu0027 anonymity can be preserved, while in other cases usersu0027 identification can be inferred with high accuracy, without relying on personal identifiable information. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10207-016-0340-2 | Int. J. Inf. Sec. |
Keywords | Field | DocType |
User identification, Anonymity, Machine learning | Computer security,Computer science,Exploit,Service provider,Network data,Personally identifiable information,Anonymity,The Internet | Journal |
Volume | Issue | ISSN |
16 | 6 | 1615-5270 |
Citations | PageRank | References |
2 | 0.37 | 36 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Apostolos Malatras | 1 | 142 | 15.18 |
| Dimitris Geneiatakis | 2 | 249 | 20.98 |
| Ioannis Vakalis | 3 | 2 | 0.37 |