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

Area of Science:

• Computer Science
• Network Security
• Data Privacy

Background:

• Wireless sensor networks (WSNs) traditionally use a many-to-one structure for data flow.
• Evolving WSN applications require many-to-many communication structures to multiple sinks.
• Existing privacy models fail to address WSNs with multiple untrusted sinks and diverse privacy requirements.

Purpose of the Study:

• To propose a novel data collection framework for WSNs.
• To enhance privacy preservation for event data in WSNs with multiple sinks.
• To address varying privacy requirements of different destination sinks.

Main Methods:

• Utilizes k-anonymity principles for data anonymization.
• Generalizes or encrypts attributes to meet sink-specific anonymity needs.
• Supports multicasting of a single anonymized output or individualized outputs to multiple sinks.

Main Results:

• Achieves differentiated privacy levels for multiple sinks simultaneously.
• Enables efficient data dissemination through multicasting.
• Reduces overall network energy consumption by optimizing data sending.

Conclusions:

• The proposed framework effectively protects sensitive event information in WSNs.
• It offers a flexible and energy-efficient solution for data collection in complex WSN architectures.
• The method enhances data security and privacy in multi-sink WSN environments.