sbb

README

Similarity-based Bucketization (SBB)

An implementation of the SBB system described in Increasing Adversarial Uncertainty to Scale Private Similarity Testing.

SBB implementation with similarity embedding retrieval and secure sketch

Two parts, a python web app and a Go web app

Setup

Server Install

• Install Cuda
• Install Anaconda
• Install Go:
  • wget https://golang.org/dl/go1.15.8.linux-amd64.tar.gz
  • sudo tar -C /usr/local -xzf go1.15.8.linux-amd64.tar.gz
  • add PATH="$PATH:/usr/local/go/bin" to ~/.bashrc
• Setup flask
• Install OPRF go package: go get -u github.com/cloudflare/circl@e69048f939ad7a5967c2a6bd8fc548bf5f0519a3
• Run pip install -r requirements.txt under server
• Copy sbb-implementation under $GOPATH/github.com/vegetable68

Client Install

• Install Go (follow the instruction above)
• go get github.com/steakknife/hamming
• Copy sbb-implementation under $GOPATH/github.com/vegetable68
• cd into sbb-implementation
• Install OPRF go package: go get -u github.com/cloudflare/circl@e69048f939ad7a5967c2a6bd8fc548bf5f0519a3
• go get -u github.com/golang/protobuf/proto@v1.4.0

Download Data

• Preprocess data with scripts under the preprocess folder.

Server Deployment

• Allow HTTP traffic
• Run go run response.go under server/goapp
• Run python app.py under server/deployment

Client Request

• Change the serverAddr value accordingly
• Run go run sbbclient.go under client

MPC Instructions/Info

• Instructions for installing Crypten can be found here
• Instruction for installing EMP can be found here
• The Crypten MPC logic can be found in mpc_local/aws_batch_classify.py
• The EMP logic can be found in emp/example.cpp. You have to clone the emp-sh2pc repo and copy that file to emp-sh2pc/test/example.cpp. By default, the command line program is configured to run locally (for testing purposes). Note that ALICE is the client and BOB is the server. To run tests across two machines, replace 127.0.0.1 with the IP address of ALICE.
• Correctness tests are in test_mpc_correctness.py and are run locally. These tests can be run by cd-ing into the mpc_local directory and running pytest.
• In order to run the Crypten and MPC tests for a particular block-list size, make sure that proper block-list is set in config.json (under filetype) and then run python mpc_run_expr.py. This will run the test for all combinations of (Crypten, MPC) x (bucketized, non-bucketized) x (matched, unmatched) and for five trials for each possibility. Outputs are written to log files in RESULTS_PATH. Summary statistics can be aggregated using client/mpc_aggregate_stats.py
• The MPC routines (for Crypten and EMP) are invoked as sub-processes on the client and the server. These sub-processes read their input from temp files and write their output to standard out. The client sends a request to the server that indicates the MPC type being used (Crypten or EMP) as well as whether bucketization is being used. The server response indicates the bucket size as this is used in the MPC protocol (and is leaked anyway through the communication cost of the protocol).
• The MPC routines XOR the output of the threshold comparison with a random bit provided by the client. This enables only the client to unblind the result of the threshold comparison. The result will appear uniformly random to the server regardless of what the true answer is.