Data collection framework for energy efficient privacy preservation in wireless sensor networks having many-to-many structures

Hayretdin, Bahsi and Levi, Albert (2010) Data collection framework for energy efficient privacy preservation in wireless sensor networks having many-to-many structures. Sensors, 10 (9). pp. 8375-8397. ISSN 1424-8220

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Abstract

Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-to-many structures evolved due to the need for conveying collected event information to multiple sinks. Privacy preserved data collection models in the literature do not solve the problems of WSN applications in which network has multiple un-trusted sinks with different level of privacy requirements. This study proposes a data collection framework bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify an event owner, are generalized or encrypted in order to meet the different anonymity requirements of sinks at the same anonymized output. If the same output is formed, it can be multicasted to all sinks. The other trivial solution is to produce different anonymized outputs for each sink and send them to related sinks. Multicasting is an energy efficient data sending alternative for some sensor nodes. Since minimization of energy consumption is an important design criteria for WSNs, multicasting the same event information to multiple sinks reduces the energy consumption of overall network.
Item Type: Article
Uncontrolled Keywords: privacy preserving data publishing; k-anonymity; wireless sensor networks
Subjects: Q Science > QD Chemistry > QD450-801 Physical and theoretical chemistry
Q Science > QD Chemistry > QD071-142 Analytical chemistry
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Albert Levi
Date Deposited: 28 Oct 2010 10:58
Last Modified: 29 Jul 2019 09:50
URI: https://research.sabanciuniv.edu/id/eprint/14942

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