  Collaborative learning allows participants to jointly train a model without
data sharing. To update the model parameters, the central server broadcasts
model parameters to the clients, and the clients send updating directions such
as gradients to the server. While data do not leave a client device, the
communicated gradients and parameters will leak a client's privacy. Attacks
that infer clients' privacy from gradients and parameters have been developed
by prior work. Simple defenses such as dropout and differential privacy either
fail to defend the attacks or seriously hurt test accuracy.
  We propose a practical defense which we call Double-Blind Collaborative
Learning (DBCL). The high-level idea is to apply random matrix sketching to the
parameters (aka weights) and re-generate random sketching after each iteration.
DBCL prevents clients from conducting gradient-based privacy inferences which
are the most effective attacks. DBCL works because from the attacker's
perspective, sketching is effectively random noise that outweighs the signal.
Notably, DBCL does not much increase computation and communication costs and
does not hurt test accuracy at all.
