12,685 research outputs found
Assessing Data Usefulness for Failure Analysis in Anonymized System Logs
System logs are a valuable source of information for the analysis and
understanding of systems behavior for the purpose of improving their
performance. Such logs contain various types of information, including
sensitive information. Information deemed sensitive can either directly be
extracted from system log entries by correlation of several log entries, or can
be inferred from the combination of the (non-sensitive) information contained
within system logs with other logs and/or additional datasets. The analysis of
system logs containing sensitive information compromises data privacy.
Therefore, various anonymization techniques, such as generalization and
suppression have been employed, over the years, by data and computing centers
to protect the privacy of their users, their data, and the system as a whole.
Privacy-preserving data resulting from anonymization via generalization and
suppression may lead to significantly decreased data usefulness, thus,
hindering the intended analysis for understanding the system behavior.
Maintaining a balance between data usefulness and privacy preservation,
therefore, remains an open and important challenge. Irreversible encoding of
system logs using collision-resistant hashing algorithms, such as SHAKE-128, is
a novel approach previously introduced by the authors to mitigate data privacy
concerns. The present work describes a study of the applicability of the
encoding approach from earlier work on the system logs of a production high
performance computing system. Moreover, a metric is introduced to assess the
data usefulness of the anonymized system logs to detect and identify the
failures encountered in the system.Comment: 11 pages, 3 figures, submitted to 17th IEEE International Symposium
on Parallel and Distributed Computin
Towards trajectory anonymization: a generalization-based approach
Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing
anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques
Privacy-Preserving Reengineering of Model-View-Controller Application Architectures Using Linked Data
When a legacy system’s software architecture cannot be redesigned, implementing
additional privacy requirements is often complex, unreliable and
costly to maintain. This paper presents a privacy-by-design approach to
reengineer web applications as linked data-enabled and implement access
control and privacy preservation properties. The method is based on the
knowledge of the application architecture, which for the Web of data is
commonly designed on the basis of a model-view-controller pattern. Whereas
wrapping techniques commonly used to link data of web applications duplicate
the security source code, the new approach allows for the controlled
disclosure of an application’s data, while preserving non-functional properties
such as privacy preservation. The solution has been implemented
and compared with existing linked data frameworks in terms of reliability,
maintainability and complexity
- …