Topology-aware Differential Privacy for Decentralized Image Classification
This work addresses privacy concerns in decentralized image classification systems, offering a novel topology-aware approach that is incremental in enhancing existing DP methods.
The paper tackles the problem of optimizing differential privacy protection in decentralized image classification systems by introducing Top-DP, which leverages network topologies to reduce noise and improve model usability, achieving a better trade-off between usability and privacy than prior works.
In this paper, we design Top-DP, a novel solution to optimize the differential privacy protection of decentralized image classification systems. The key insight of our solution is to leverage the unique features of decentralized communication topologies to reduce the noise scale and improve the model usability. (1) We enhance the DP-SGD algorithm with this topology-aware noise reduction strategy, and integrate the time-aware noise decay technique. (2) We design two novel learning protocols (synchronous and asynchronous) to protect systems with different network connectivities and topologies. We formally analyze and prove the DP requirement of our proposed solutions. Experimental evaluations demonstrate that our solution achieves a better trade-off between usability and privacy than prior works. To the best of our knowledge, this is the first DP optimization work from the perspective of network topologies.