  As users increasingly rely on cloud-based computing services, it is important
to ensure that uploaded speech data remains private. Existing solutions rely
either on server-side methods or focus on hiding speaker identity. While these
approaches reduce certain security concerns, they do not give users client-side
control over whether their biometric information is sent to the server. In this
paper, we formally define client-side privacy and discuss its three unique
technical challenges: (1) direct manipulation of raw data on client devices,
(2) adaptability with a broad range of server-side processing models, and (3)
low time and space complexity for compatibility with limited-bandwidth devices.
Solving these challenges requires new models that achieve high-fidelity
reconstruction, privacy preservation of sensitive personal attributes, and
efficiency during training and inference. As a step towards client-side privacy
for speech recognition, we investigate three techniques spanning signal
processing, disentangled representation learning, and adversarial training.
Through a series of gender and accent masking tasks, we observe that each
method has its unique strengths, but none manage to effectively balance the
trade-offs between performance, privacy, and complexity. These insights call
for more research in client-side privacy to ensure a safer deployment of
cloud-based speech processing services.
