PPDO: Privacy-Preservation-Aware Delay Optimization for Task Offloading in Collaborative Edge Computing
Collaborative edge computing (CEC) improves mobile edge computing (MEC) by distributing computational tasks across multiple edge nodes. However, a significant challenge remains: protecting user privacy during task offloading.
Most research concentrates on reducing task offloading costs like execution delay and energy consumption, often neglecting privacy protection. The limited studies that consider privacy primarily focus on location privacy, overlooking usage pattern privacy. Moreover, methods that secure both privacy types tend to cause extra delays.
To address these issues, a research team from the College of Computer Science and Engineering at Guilin University of Technology and the Guangxi Key Laboratory of Embedded Technology and Intelligent System developed a privacy-preservation-aware delay optimization task-offloading algorithm (PPDO) specifically for CEC systems.
"In the field of collaborative edge computing (CEC), while the technology enhances the performance of mobile edge computing (MEC) by distributing computational tasks across distributed edge nodes, it faces a prominent challenge: user privacy leakage during task offloading."
"Even the few studies addressing privacy issues often only safeguard location privacy, ignoring usage pattern privacy, or introduce additional delays when protecting both types of privacy."
Summary
The PPDO algorithm advances collaborative edge computing by minimizing delay while protecting users’ privacy in task offloading, balancing performance and security.