TY - GEN
T1 - Access-Private outsourcing of markov chain and randomWalk based data analysis applications
AU - Lin, Ping
AU - Candan, Kasim
N1 - Publisher Copyright:
© 2006 IEEE.
PY - 2006
Y1 - 2006
N2 - Random walk graph and Markov chain based models are used heavily in many data and system analysis domains, including web, bioinformatics, and queueing. These models enable the description and analysis of various behaviors of stochastic systems. If the system being modelled has certain properties, such as if it is irreducible and aperiodic, close form formulations corresponding to its stationary behavior can be used to analyze its behavior. However, if the system does not have these properties or if the user is not interested in the stationary behavior, then an iterative approach needs to be used to determine potential outcomes based on the initial probability distribution inputs to the model. In this paper, we focus on access-privacy enabled outsourced Markov chain based data analysis applications, where a non-Trusted service provider takes (hidden) user queries that are described in terms of initial state distributions, and evaluates them iteratively in an oblivious manner. We show that this iterative process can leak information regarding the possible values of the hidden input if the server has a priori knowledge about the underlying Markovian process. Hence as opposed to simple obfuscation mechanisms, we develop an algorithm based on methodical addition of extra states, which guarantees unbounded feasible regions for the inputs, thus preventing a malicious host from having an informed guess regarding the inputs.
AB - Random walk graph and Markov chain based models are used heavily in many data and system analysis domains, including web, bioinformatics, and queueing. These models enable the description and analysis of various behaviors of stochastic systems. If the system being modelled has certain properties, such as if it is irreducible and aperiodic, close form formulations corresponding to its stationary behavior can be used to analyze its behavior. However, if the system does not have these properties or if the user is not interested in the stationary behavior, then an iterative approach needs to be used to determine potential outcomes based on the initial probability distribution inputs to the model. In this paper, we focus on access-privacy enabled outsourced Markov chain based data analysis applications, where a non-Trusted service provider takes (hidden) user queries that are described in terms of initial state distributions, and evaluates them iteratively in an oblivious manner. We show that this iterative process can leak information regarding the possible values of the hidden input if the server has a priori knowledge about the underlying Markovian process. Hence as opposed to simple obfuscation mechanisms, we develop an algorithm based on methodical addition of extra states, which guarantees unbounded feasible regions for the inputs, thus preventing a malicious host from having an informed guess regarding the inputs.
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U2 - 10.1109/ICDEW.2006.25
DO - 10.1109/ICDEW.2006.25
M3 - Conference contribution
AN - SCOPUS:84990935227
T3 - ICDEW 2006 - Proceedings of the 22nd International Conference on Data Engineering Workshops
SP - 97
BT - ICDEW 2006 - Proceedings of the 22nd International Conference on Data Engineering Workshops
A2 - Barga, Roger S.
A2 - Zhou, Xiaofang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference on Data Engineering Workshops, ICDEW 2006
Y2 - 3 April 2006 through 7 April 2006
ER -