An implementation of the FrodoPIR Private Information Retrieval scheme. Find the details over our eprint paper.
We design FrodoPIR, a highly configurable, stateful, single-server Private Information Retrieval (PIR) scheme that involves an offline phase that is completely client-independent. Coupled with small online overheads, it leads to much smaller amortized financial costs on the server-side than previous approaches. In terms of performance for a database of 1 million KB elements, FrodoPIR requires <1 second for responding to a client query, has a server response size blow-up factor of > 3.6x, and financial costs are ~$1 for answering client queries. Our experimental analysis is built upon a simple, non-optimized Rust implementation, illustrating that FrodoPIR is eminently suitable for large practical deployments.
Warning: This code is a research prototype. Do not use it in production.
The source code can be built, tested, and benchmarked using Docker.
In order to natively build, run, test and benchmark the library, you will need the following:
Rust >= 1.61.0
Cargo
Make
Python3 >= 3.9.7
Build Docker image:
docker build -t frodo-pir .
Build and run tests:
docker run --rm frodo-pir
Run Docker image interactively (from here, you can run any of the make
commands below):
docker run --rm -it --entrypoint /bin/bash frodo-pir
To install the latest version of Rust, use the following command (you can also check how to install on the Rust documentation):
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
To build the library, run:
make build
To run the tests:
make test
To view documentation (in web browser):
make docs
To run a specific set of benchmarks, run (note the this process is slow):
make bench
This will execute client query benchmarks and DB generation benchmarks (for more details, see the benches/bench.rs
file).
To run all benchmarks (note that this process is very slow):
make bench-all
The src
folder contains the main FrodoPIR functionality. In particular:
api.rs
: provides the main FrodoPIR API:- To read and generate the appropriate parameters:
from_json_file
(from a file) orfrom_base64_strings
(from strings). (This corresponds to the 'Server setup' and 'Server preprocessing' phases from the paper). - To prepare and create the client query:
prepare_query
(this corresponds to the 'Client query generation' phase from the paper). - To analyse the client query and create the server response:
respond
(this corresponds to the 'Server response' phase from the paper).
- To read and generate the appropriate parameters:
- The
db.rs
file contains the main functionality to be used for database processing. - The
util.rs
file contains utility functions.
The data
directory contains a python script used to generate the needed data for testing.
The benchs
directory contains a script used to benchmark the library.
The pi-rs-cli-utils
directory contains files of 'utility' functionality.
An easy way to see how to use the library can be found on the tests on the api.rs
file. For full code documentation of the code, run make docs
.
The following code is a copy of the aforementioned test. It exemplifies how FrodoPIR can be used by mocking both a server and a client. First, the server generates all the needed parameters in relationship to a mocked random database. Then, the client downloads said parameters and prepares queries (as part of the for-loop). Then, both server and client interact such that the server outputs the correct response. In order to run this example, one can run the tests.
use frodo_pir::api::*;
fn client_query_e2e() {
/* Preprocessing performed by the server */
// The LWE dimension to use
let lwe_dim = 1572;
// The number of rows in the database
let m = 2u32.pow(16) as usize;
// The length of each element in the database
let ele_size = 2u32.pow(13) as usize;
// The number of plaintext bits to use in each matrix element
// - 10 bits, for 16 ≤ log2(m) ≤ 18
// - 9 bits, for log2(m) ≤ 20
// see Section 5 of paper for full details
let plaintext_bits = 10usize;
// Generates a random database
let db_eles = generate_db_eles(m, (ele_size + 7) / 8);
let db = Shard::from_base64_strings(&db_eles, lwe_dim, m, ele_size, plaintext_bits);
// Parameters used by the server
let base_params = db.get_base_params();
// Public parameters downloaded by the client
let common_params = CommonParams::from(base_params);
/* Run client queries */
for i in 0..10 {
// Preprocess client queries before knowing query index (can be done offline)
let mut query_params = QueryParams::new(&common_params, base_params);
// Generate client query for index `i` of database
let query = query_params.prepare_query(i);
// Server response to query
let d_resp = db.respond(&query).unwrap();
// Client post-processing of server response
let resp: Response = bincode::deserialize(&d_resp).unwrap();
let output = resp.parse_output_as_base64(&query_params);
// Check that client output matches row `i` of server DB.
assert_eq!(output, db_eles[i]);
}
}
We have two big tests that the library executes:
client_query_to_server_10_times()
test which executes the client-to-server functionality: the client asks for an item in the database and the server is able to privately return it. The test asserts that the returned item is indeed the correct item in the database. It executes a for loop 10 times.client_query_to_server_attempt_params_reuse
test which executes the client-to-server functionality one time. It asserts that once parameters for a query are used, they are marked as so, and cannot be reused.
@misc{cryptoeprint:2022/949,
author = {Alex Davidson and Gonçalo Pestana and Sofía Celi},
title = {FrodoPIR: Simple, Scalable, Single-Server Private Information Retrieval},
howpublished = {Cryptology ePrint Archive, Paper 2022/981},
year = {2022},
note = {\url{https://eprint.iacr.org/2022/981}},
url = {https://eprint.iacr.org/2022/981}
}