In this paper, we propose a task scheduling strategy, which can achieve image feature extraction on edge while ensuring privacy. Our task scheduling strategy applies to a fairly popular privacy-preserving Scale-Invariant Feature Transform SIFT scheme, where images to be processed are firstly randomly split into two portions for encryption and transmitted to two different edge nodes for feature extraction. Then, in the edge, our task scheduling strategy will re-assign these two portions to proper edge nodes for processing. During the whole process, two portions of the same image should not be assigned to the same edge node in order to preserve privacy. We show that this privacy constraint can be enforced through constructing a pairwise Markov chain, and carefully designing system states and transition probabilities. We further formulate the whole task scheduling problem as a stochastic latency minimization problem and solve it by converting it into a linear programming problem. Simulation results show that our proposed task scheduling strategy can achieve lower latency than baseline strategies while satisfying the privacy constraint.
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