250 citations · 1.1k across the 76 of their papers we have counts for
5 papers · 1 filter
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference
Maximilian Lam, Michael Mitzenmacher, Vijay Janapa Reddi +2
Multiparty computation approaches to secure neural network inference commonly rely on garbled circuits for securely executing nonlinear activation functions. However, garbled circu…
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
Maximilian Lam, Gu-Yeon Wei, David Brooks +2
We show that aggregated model updates in federated learning may be insecure. An untrusted central server may disaggregate user updates from sums of updates across participants give…
Oblivious Storage with Low I/O Overhead
Michael T. Goodrich, Michael Mitzenmacher, Olga Ohrimenko +1
We study oblivious storage (OS), a natural way to model privacy-preserving data outsourcing where a client, Alice, stores sensitive data at an honest-but-curious server, Bob. We sh…
Oblivious RAM Simulation with Efficient Worst-Case Access Overhead
Michael T. Goodrich, Michael Mitzenmacher, Olga Ohrimenko +1
Oblivious RAM simulation is a method for achieving confidentiality and privacy in cloud computing environments. It involves obscuring the access patterns to a remote storage so tha…
Privacy-Preserving Group Data Access via Stateless Oblivious RAM Simulation
Michael T. Goodrich, Michael Mitzenmacher, Olga Ohrimenko +1
We study the problem of providing privacy-preserving access to an outsourced honest-but-curious data repository for a group of trusted users. We show that such privacy-preserving d…