fuzzytags/README.md

# FuzzyTags

Anonymous messaging systems (and other privacy-preserving applications) often require a mechanism for one party
to learn that another party has messaged them ("notifications").

Many schemes rely on a bandwidth-intensive "download everything and attempt-decryption" approach. Others rely on a trusted
3rd party, or various non-collusion assumptions, to provide a "private" service. Other schemes
require that parties arrange themselves in "buckets" or "mailboxes" effectively creating smaller instances of the
"download everything" approach.

It would be awesome if we could get an **untrusted**, **adversarial** server to do the work for us 
without compromising metadata-resistance or requiring parties to split themselves into buckets (effectively
dividing the anonymity set of the system)!

![](https://git.openprivacy.ca/openprivacy/fuzzytags/media/branch/trunk/FuzzyTags_Logo.png)

**fuzzytags** is an experimental probabilistic cryptographic tagging structure to do just that! 

Instead of placing messages into deterministic buckets based on the recipient, **fuzzytags** allow each
message to probabilistically address itself to several parties in addition to the intended party - utilizing the
anonymity of the whole set of participants, instead of the ones who happen to share a bucket for a given round.

Specifically **fuzzytags** provide the following properties:

 * Correctness: Valid tags constructed for a specific tagging key will always validate when tested using a 
 derived detection key.
 * Fuzziness: Tags will produce false positive matches with probability _p_ related to the security property (_γ_) when 
 tested against detection keys they were not intended for.
 * Security: An adversarial server with access to the detection key **is unable to distinguish false
  positives from true positives**. (this property is referred to as *Detection Ambiguity*)

## Security (hic sunt dracones)

This crate provides an experimental implementation of the `FMD2` scheme described in ["Fuzzy Message Detection"](https://eprint.iacr.org/2021/089). Using
Ristretto as the prime order group.

This code has not undergone any significant review. 

Further, the properties provided by this system are highly dependent on selecting a **false positive rate** _p_ and 
**scheme constant** _γ_ for your system. There is no one-size-fits-all approach.

If _p_ is too low, then the probability of false positives for a given party will be very high.

If _p_ is too high, then an adversarial server will be able to link messages to recipients with probability
approaching _1_. 

Likewise a large _γ_ means higher bandwidth costs, but a small _γ_ reveals more of the root secret to the server while
also increasing the change of perfect (but false) matches across all parties.

We are also [building a simulator](https://git.openprivacy.ca/openprivacy/fuzzytags-sim) to understand these
parameter choices in addition to other factors when deploying fuzzytags to real-world systems.

For more guidance (and warnings) on integrating fuzzytags into a privacy preserving application see [documentation](https://docs.rs/fuzzytags/#integrating-fuzzytags)

## Building

This crate requires experimental features currently only provided by Rust nightly:

` rustup default nightly`

## Terminology and a more detailed System Description

There exists a metadata resistant application that uses untrusted servers to mediate communication between parties. 

Each party can be identified with a set of cryptographic identifiers and there exists methods in or external to the system
to distribute keys securely and authentically.

Now, instead of each party adopting a download-everything approach to metadata privacy (or invoking non-collusion
or other assumptions) we can leverage fuzzytags to reduce the number of messages downloaded from the server by each party
while maintaining a formalized concept of metadata privacy.

Every party generates a `RootSecret`, from which they can derive a `DetectionKey` and a `TaggingKey`. These keys will
be generated with a parameter _γ_ that relates to the minimum false-positive probability 2^-γ.

When submitting messages to the server for an intended **recipient**, the **sender** will generate a new tag
from the  **recipients** `TaggingKey`.

All parties will `extract` a `DetectionKey` from their key pair. This key will be of length `n` and provide
a false positive detection probability of  0 <= 2^-n <= 2^-γ. This detection key can be given to an adversarial server.

When fetching new messages from the adversarial server, the server first runs a `test` of the tag of the message against
the parties' detection key. If the tag passes the test, the message (along with the tag) is provided to the **recipient**.

Finally, the **recipient** runs their own `test` of the tag against an extracted detection key such that 
the probability of a false positive will be 2^-n == 2^-γ. This will
produce a subset of messages likely intended for the **recipient**, with a smaller probability of false positives.

Alternatively the **recipient**  can simply try and decrypt every message in the subset of messages that the server
provided them (depending on the efficiency of the decryption method).

## Usage

A party first needs to generate `RootSecret`
    
    use fuzzytags::RootSecret;
    use rand::rngs::OsRng;
    let mut rng = OsRng;
    let secret = RootSecret::<24>::generate(&mut rng);

From the secret detection key a party can derive a `DetectionKey` which can be given to adversarial server to
fuzzily detect tags on behalf of the party.

From the secret detection key a party can also derive a `TaggingKey` that can be public and given to
other parties for the purpose of generating fuzzytags addressed to a given party.

The `24` in the above code is a security property (_γ_) in the system. For a given gamma, a tag generated for a specific public key will
validate against a random public key with a maximum probability of _2^-gamma_.

## Generating Tags

Once in possession of a tagging key, a party in a metadata resistant app can use it to generate tags:
    
        use fuzzytags::RootSecret;
        use rand::rngs::OsRng;
        let mut rng = OsRng;
        let secret = RootSecret::<24>::generate(&mut rng);
        let tagging_key = secret.tagging_key();
        
        // Give public key to a another party...
        // and then they can do...
        let tag = tagging_key.generate_tag(&mut rng);

These tags can then be attached to a message in a metadata resistant system.

## Testing Tags

First it is necessary to extract a detection key for a given false positive probability _0 <= 2^-n <= 2^-γ_.

This extracted key can then be given to an adversarial server. The server can then test a given tag against the detection key e.g.:

        use fuzzytags::RootSecret;
        use rand::rngs::OsRng;
        let mut rng = OsRng;
        let secret = RootSecret::<24>::generate(&mut rng);
        let tagging_key = secret.tagging_key();
        // extract a detection key
        let detection_key = secret.extract_detection_key(5);
        
        // Give the tagging key to a another party...
        // and then they can do...
        let tag = tagging_key.generate_tag(&mut rng);
        
        // The server can now do this:
        if detection_key.test_tag(&tag) {
            // the message attached to this tag *might* be for the party associated with the detection key 
        } else {
            // the message attached to this tag is definitely *not* for the party associated with the detection key.
        }

## Entangled Tags

When enabled with the `entangled` feature the `TaggingKey::generate_entangled_tag` function is available. This
allows you to generate tags that will validate against **multiple** detection keys from **distinct tagging keys** and
opens up applications like **multiple broadcast** and **deniable sending**.


       use fuzzytags::{RootSecret, TaggingKey};
       use rand::rngs::OsRng;
       let mut rng = OsRng;
       let secret_1 = RootSecret::<24>::generate(&mut rng);
       let secret_2 = RootSecret::<24>::generate(&mut rng);
       let tagging_key_1 = secret_1.tagging_key(); // give this to a sender
       let tagging_key_2 = secret_2.tagging_key(); // give this to a sender
       // Will validate for detection keys derived from both secret_1 and secret_2 up
       // to n=8
        #[cfg(feature = "entangled")]
       let tag = TaggingKey::generate_entangled_tag(vec![tagging_key_1,tagging_key_2], &mut rng, 8);

## Serialization

This crate relies on `serde` for serialization. FuzzyTags are first compressed into a byte array of 64 bytes +
`γ` bits, padded to the end with zeros to the nearest byte. This representation can then be exchanged using a number
of different approaches e.g.: 

    use fuzzytags::RootSecret;
    use fuzzytags::Tag;
    use rand::rngs::OsRng;

    let mut rng = OsRng;
    let secret = RootSecret::<24>::generate(&mut rng);
    let tagging_key = secret.tagging_key();

    // Give public key to a another party...
    // and then they can do...
    let tag = tagging_key.generate_tag(&mut rng);
    
    // An example using JSON serialization...see serde doc for other formats:
    let serialized_tag = serde_json::to_string(&tag).unwrap();
    println!("Serialized: {}", serialized_tag);

    // We can then deserialize with:
    let deserialized_tag: Result<Tag<24>, serde_json::Error> = serde_json::from_str(&serialized_tag);
    println!("Deserialized: {}", deserialized_tag.unwrap());

## Benchmarks 

We use [criterion](https://crates.io/crates/criterion) for benchmarking, and benchmarks can run using `cargo bench --bench fuzzy_tags_benches`
    
Results will be in `target/criterion/report/index.html`.

To benchmark entangled tags run:

`cargo bench --features "entangled" --bench entangled`

### AVX2

This crate has support for the avx2 under the feature `simd`, to take advantage of this feature it is
necessary to build with `RUSTFLAGS="-C target_feature=+avx2"` e.g.

`env RUSTFLAGS="-C target_feature=+avx2" cargo test --release --features "bulk_verify,entangled,simd"`

This results in a 40%+ performance improvements on the provided benchmarks.

## Credits and Contributions

- Based on [Fuzzy Message Detection](https://eprint.iacr.org/2021/089) by Gabrielle Beck and Julia Len and Ian Miers and Matthew Green
- Performance & API improvements contributed by Henry de Valence
- Universal Tag Bug found by [Lee Bousfield](https://github.com/PlasmaPower/)
- Fuzzytags Logo by [Marcia Díaz Agudelo](https://www.instagram.com/marcia_ilustra/) 
- Thanks to Henry de Valence, George Tankersly, Lee Bousfield and others for helpful discussions.