Protecting Adaptive Sampling from Information Leakage on Low-Power Sensors

Tejas Kannan, Henry Hoffmann

Abstract:

Adaptive sampling is a powerful family of algorithms for managing
energy consumption on low-power sensors. These algorithms
use captured measurements to control the sensor’s collection rate,
leading to near-optimal error under energy constraints. Adaptive
sampling’s data-driven nature, however, comes at a cost in privacy.
In this work, we demonstrate how the collection rates of general
adaptive policies leak information about captured measurements.
Further, individual adaptive policies display this leakage on multiple
tasks. This result presents a challenge in maintaining privacy for
sensors using energy-efficient batched communication. In this context,
the size of measurement batches exposes the sampling policy’s
collection rate. Thus, an attacker who monitors the encrypted link
between sensor and server can use message lengths to uncover information
about the captured values. We address this side-channel
by introducing a framework called Adaptive Group Encoding (AGE)
that protects any periodic adaptive sampler. AGE uses quantization
to encode all batches as fixed-length messages, making message
sizes independent of the collection rate. AGE reduces the quantization
error through a series of transformations. The proposed
framework preserves the low error of adaptive sampling while
preventing information leakage and incurring negligible energy
overhead.