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.
Highlights
Recent technological advances produce low cost wireless sensors for observing many physical phenomena of the world like temperature, humidity etc
Privacy preserving data collection framework is proposed for Wireless sensor networks (WSNs)
In order to meet the requirements of network and threat model, we propose a method called Iterative k-Anonymous Clustering Method (Ik-ACM)
Summary
Recent technological advances produce low cost wireless sensors for observing many physical phenomena of the world like temperature, humidity etc. WSNs generally have many-to-one structure so that sensors collect event information from the area and send to a unique sink. Data collection models do not meet the requirements of having many data collectors with different privacy levels, which may be the case in a WSN. This issue has to be dealt by WSN designers. Privacy preserving data collection framework is proposed for WSNs. The framework is based on a network model which has multiple un-trusted sinks. Privacy requirement level of each sink is assumed to be different from each other, which can be a realistic scenario in recent WSN applications.
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