An adaptive UNIX command-line assistant
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
Masquerade Detection Using Truncated Command Lines
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
WAIMW '06 Proceedings of the Seventh International Conference on Web-Age Information Management Workshops
Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds
Proceedings of the 16th ACM conference on Computer and communications security
Controlling data in the cloud: outsourcing computation without outsourcing control
Proceedings of the 2009 ACM workshop on Cloud computing security
Changing the rules: a comprehensive approach to theory refinement
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Experiments in UNIX command prediction
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Hybrid method for detecting masqueraders using session folding and hidden markov models
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Security Challenges for the Public Cloud
IEEE Internet Computing
Intrusion detection via analysis and modelling of user commands
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Masquerade detection via customized grammars
DIMVA'05 Proceedings of the Second international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
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When operating from the cloud, traces of user activities and behavioral patterns are accessible to anyone with enough privileges within the system. This could be, for example, the case of dishonest technical staff who may well be interested in selling user logs to competitors. In this paper, we investigate some of the security and privacy leakages derived from the analysis of user activities. We show that the working behavioral patterns exhibited by users can be easily captured into computationally useful representations that would allow an adversary to predict future activities, detect the occurrence of events of interest, or infer the organization's internal structure. We then introduce the idea of obfuscating user behaviour through Online Action Randomization Algorithms. In doing so, we introduce an indistinguishability-based definition for perfectly obfuscated actions and a concrete scheme to randomize user traces in an incremental way. We report experimental results confirming the obfuscation quality and other properties of the proposed schemes.