Understanding differential privacy for ATR template generation

Compromised target recognition systems could potentially leak important information about their training data. Preventing such a compromise is critical in maintaining operational security. As a potential mitigation, differential privacy methods can provide theoretical guarantees on dataset privacy. In this work, we examine how differentially private methodologies can be integrated into an ATR pipeline. In particular, we focus on the process of generating ATR templates that are differentially private as a means of protecting hypothetically sensitive training data. We provide an analysis of synthetic aperture radar templates using a distributional model, common in statistical ATR methods such as multinomial pattern matching. Our work provides an analysis of both the template generation itself as well as statistical tests derived from the generated templates, and we observe the steep costs of high-dimensional templates. By understanding the effect differential privacy has on template generation, we can begin to characterize the performance/privacy trade-offs of differentially private ATR.