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Sarah Jamie Lewis | 78f780a389 |
10
Cargo.toml
10
Cargo.toml
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@ -1,7 +1,7 @@
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[package]
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name = "fuzzytags"
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description = "a probabilistic cryptographic structure for metadata resistant tagging"
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version = "0.6.0"
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version = "0.5.0"
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repository = "https://git.openprivacy.ca/openprivacy/fuzzytags"
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authors = ["Sarah Jamie Lewis <sarah@openprivacy.ca>"]
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edition = "2018"
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[dependencies]
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hex = "0.4.2"
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rand_core = "0.5.0"
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rand ="0.7"
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curve25519-dalek = { package = "curve25519-dalek", version="3.2", features=["serde"]}
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rand_core = "0.6.0"
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rand ="0.8.3"
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curve25519-dalek = { package = "curve25519-dalek-ng", version="4.0.1", features=["serde"]}
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sha3 = "0.9.1"
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serde = {version="1.0.123", features=["derive"]}
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bit-vec = {version="0.6.3"}
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brute-force = {version="0.1.1", features=["curve25519"], optional=true}
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brute-force = {git="https://git.openprivacy.ca/sarah/brute-force.git", version="0.2.0", features=["curve25519"], optional=true}
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rayon = {version="1.5.0", optional=true}
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[dev-dependencies]
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11
README.md
11
README.md
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@ -52,11 +52,6 @@ parameter choices in addition to other factors when deploying fuzzytags to real-
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For more guidance (and warnings) on integrating fuzzytags into a privacy preserving application see [documentation](https://docs.rs/fuzzytags/#integrating-fuzzytags)
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## Building
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This crate requires experimental features currently only provided by Rust nightly:
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` rustup default nightly`
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## Terminology and a more detailed System Description
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@ -194,14 +189,10 @@ of different approaches e.g.:
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## Benchmarks
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We use [criterion](https://crates.io/crates/criterion) for benchmarking, and benchmarks can run using `cargo bench --bench fuzzy_tags_benches`
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We use [criterion](https://crates.io/crates/criterion) for benchmarking, and benchmarks can run using `cargo bench`
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Results will be in `target/criterion/report/index.html`.
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To benchmark entangled tags run:
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`cargo bench --features "entangled" --bench entangled`
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### AVX2
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This crate has support for the avx2 under the feature `simd`, to take advantage of this feature it is
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use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
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use fuzzytags::{RootSecret};
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use fuzzytags::{RootSecret, TaggingKey};
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use rand::rngs::OsRng;
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use std::time::Duration;
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@ -1,6 +1,7 @@
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#![deny(missing_docs)]
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#![doc = include_str!("../README.md")]
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#![doc = include_str!("../ANONYMITY.md")]
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#![feature(external_doc)]
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#![doc(include = "../README.md")]
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#![doc(include = "../ANONYMITY.md")]
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#![doc(html_logo_url = "https://git.openprivacy.ca/openprivacy/fuzzytags/media/branch/trunk/FuzzyTags_Logo.png")]
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use bit_vec::BitVec;
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use curve25519_dalek::constants::RISTRETTO_BASEPOINT_POINT;
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@ -533,7 +534,7 @@ impl<const GAMMA: u8> TaggingKey<{ GAMMA }> {
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// Once the ciphertext has been computed, we use a master trapdoor for the chameleon hash (which is part of the scheme’s DetectionKey) in order to compute a collision (y,m) where m
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// is a hash of the remaining components of the ciphertext"
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// Translated, m is a challenge over the random element u and the ordered ciphertexts
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// Translated m is a challenge over the random element u and the ordered ciphertexts
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// It is then used to construct a response y which can be used to recover w the random element
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// used to derive the key.
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