This survey builds a unified threat model and evaluation framework for privacy-preserving distributed learning in IoT settings, where devices share model updates rather than raw data but still risk leaking information through gradients or activations. The authors compare techniques like differential privacy, homomorphic encryption, and lightweight Bloom-filter encodings on both privacy robustness (resistance to gradient leakage, membership inference, etc.) and system efficiency (compute, memory, communication overhead). They highlight the fundamental trade-off: strong cryptographic methods are expensive, while lightweight methods (e.g., Bloom filters) offer weaker privacy through collision-induced ambiguity but stay practical on resource-constrained devices.
Main takeaways:
- Defines a unified threat model covering gradient leakage, model inversion, membership inference, and communication-based attacks.
- Compares differential privacy, homomorphic encryption, secure multi-party computation, and Bloom-filter encodings under realistic IoT resource constraints.
- Bloom-filter methods provide lightweight privacy via collision ambiguity with low computational and communication overhead.
- Strong privacy guarantees come at high system cost; practical deployments must navigate the privacy-efficiency trade-off.