private-ann

Private Approximate Nearest Neighbor Search

Prototype implementation of the two-server privacy-preserving ANN search protocol with malicious security.

Paper: https://eprint.iacr.org/2021/1157 (IEEE Security and Privacy 2022)

Running the experiments

Dependencies

• GMP Library: On Ubuntu run sudo apt-get install libgmp3-dev. On yum, sudo yum install gmp-devel.
• Go v1.16 or later: On Ubuntu run sudo apt-get install golang-go. On yum, sudo yum install golang.
• OpenSSL: On Ubuntu run sudo apt-get install libssl-dev. On yum, sudo yum install openssl-devel.
• Make: On Ubuntu run sudo apt install make. On yum, sudo yum install make.

For optimal performance, you should compile the C code with clang-12 (approximately 10-20 percent faster than the default on some distributions).

• Clang-12: On Ubuntu run sudo apt install clang-12. On yum, sudo yum install clang.
  • You'll also need llvm if you use clang.
• LLVM-AR: On Ubuntu run sudo apt install llvm. On yum, sudo yum install llvm.

Datasets

All datasets used in the paper can be obtained from https://github.com/erikbern/ann-benchmarks.

The raw data is in HDF5 format which can be converted to CSV using /datasets/dataconv.py script. The python script generates three files prefixed by the dataset name. For example, python dataconv.py deep1b.hdf5 will output deep1b_train.csv, deep1b_test.csv, and deep1b_neighbors.csv.

The bash script argument requires DATASET_PATH point to the directory where these three files are located as well as the dataset name predix. For example, to run the server on the deep1b data, setDATASET_PATH=/home/user/datasets/deep1b (note the lack of suffix in the dataset file name). The code will automatically locate and use the training data to build the data structure and the test data as "queries" issued by clients. Note that generating the hash tables for the first time can take a while; we recommend caching the results.

Running the servers

Both servers must have access to the same datasets so that they can locally compute the necessary data structure. Each server is run automatically using the provided scripts. There is one script per dataset. Each script will cycle through all experimental configurations (e.g., number of hash tables, number of probes, etc.).

On each server machine

1. (optional) Set the C compiler to the corresponding compiler for cgo compilation.

export CC=clang-12

1. Compile the C DPF library which is used by the Go code.

cd ~/go/src/private-ann/pir/dpfc/src
make

1. Download and process the datasets, placing each dataset into ~/go/src/private-ann/datasets/.

On server machine A

cd scripts
bash mnist.sh --sid 0

On server machine B

cd scripts
bash mnist.sh --sid 1

Running the client

After configuring client.sh with the server IP addresses, run

bash client.sh

which will query the servers and save the experiment results to ../results/ under a random .json file. To continuously query the servers (until all parameter combinations are exhausted), run

bash clicycle.sh

which will spin up a new client once the servers have initialized the new experiment configuration.

Finding dataset parameters (Optional)

Note that all paramters are already pre-computed (located in /ann/cmd/meanAndStd/). However, follow the below steps if you would like to recompute or change the way the dataset parameters are generated.

First go to the parameters directory

cd ann/cmd/parameters
go build

To find the mean and standard deviation of the brute force distances for a dataset

./parameters --dataset ../../../datasets/mnist --dimension 24

The dimension 24 argument cause dimensionality reduction to 24 dimensions before calculating the distances, so the radii will account for the variance introduced by the reduction. This is the form expected by the implementation of the 24 dimensional Leech Lattice LSH.

Checking hash function accuracy

First go to the accuracy directory

cd ann/cmd/accuracy
go build

The accuracy script accepts parameters for many aspects of the LSH. For example

./accuracy --dataset=../../../datasets/mnist --tables=10 --probes=30 --projectionwidthmean=887.77 --projectionwidthstddev=244.92 --mode=test --sequencetype=normal2

Evaluates the 10000 test queries for accuracy under approximation factor 2 for the MNIST dataset. The values of width and stddev are those found with the parameter program. To use training data to modify parameters, first run the parameter program to generate an answer set, move it into the directory, and use --mode=train. Sequence type provides slightly different options for computing the radii.

The test.py python file contains the parameters used to run the experiments.

Plotting!

Plot the LSH radii and vector distribution of each dataset.

You can plot the parameters for each dataset (LSH radii, etc.) using the plot_radii.py script.

python plot_radii.py --file ../ann/cmd/parameters/mnist24x10000Data.txt --name mnist --mnist
python plot_radii.py --file ../ann/cmd/parameters/deep1b24x10000Data.txt --name deep1b
python plot_radii.py --file ../ann/cmd/parameters/sift24x10000Data.txt --name sift
python plot_radii.py --file ../ann/cmd/parameters/gist24x10000Data.txt --name gist --maxdist 1.5

Plot hash function accuracy

You can download the results of all accuracy experiments (zip file) from the following Google Drive link: https://drive.google.com/file/d/1vBfVOfjWYn-B5F1xH5_GErr-ZTuLjMfb/view?ts=61b8d3d2 Alternate download (Dropbox) link: https://www.dropbox.com/s/dwwqs6qo4ukituj/results.zip?dl=0

unzip results.zip
mv scripts/acc_plot.py results
cd results
python acc_plot.py

Plot latency

The raw data is available in the paper_results directory. To plot it, simply run:

python plot_runtime.py --file ../paper_results/mnist_results.json --mnist --cap -1
python plot_runtime.py --file ../paper_results/deep1b_results.json
python plot_runtime.py --file ../paper_results/sift_results.json
python plot_runtime.py --file ../paper_results/gist_results.json

Plot PBR overheads

python pbrsim.py

Important Warning

This implementation is intended as a proof-of-concept prototype only! The code was implemented for research purposes and has not been vetted by security experts. As such, no portion of the code should be used in any real-world or production setting!

Acknowledgements

• Simon Langowski is a co-contributor to the LSH and DPF implementations.
• Parts of the DPF code are based on the C implementation of the Dory system.

Citation

@inproceedings{preco,
  title={Private approximate nearest neighbor search with sublinear communication},
  author={Servan-Schreiber, Sacha and Langowski, Simon and Devadas, Srinivas},
  booktitle={2022 IEEE Symposium on Security and Privacy (SP)},
  pages={911--929},
  year={2022},
  organization={IEEE}
}