xaynet 0.9.0

The Xayn Network project is building a privacy layer for machine learning so that AI projects can meet compliance such as GDPR and CCPA. The approach relies on Federated Learning as enabling technology that allows production AI applications to be fully privacy compliant.
Documentation 
use crate::PetError;

#[derive(Debug, PartialEq)]
#[cfg_attr(test, derive(Clone))]
/// A dummy certificate.
pub struct Certificate(Vec<u8>);

#[allow(clippy::len_without_is_empty)]
impl Certificate {
    #[allow(clippy::new_without_default)]
    /// Create a certificate
    pub fn new() -> Self {
        Self(vec![0_u8; 32])
    }

    /// Get the length of the certificate.
    pub fn len(&self) -> usize {
        self.as_ref().len()
    }

    /// Validate a certificate
    pub fn validate(&self) -> Result<(), PetError> {
        Ok(())
    }
}

impl AsRef<[u8]> for Certificate {
    /// Get a reference to the certificate.
    fn as_ref(&self) -> &[u8] {
        self.0.as_slice()
    }
}

impl From<Vec<u8>> for Certificate {
    /// Create a certificate from bytes.
    fn from(bytes: Vec<u8>) -> Self {
        Self(bytes)
    }
}

impl From<&[u8]> for Certificate {
    /// Create a certificate from a slice of bytes.
    fn from(slice: &[u8]) -> Self {
        Self(slice.to_vec())
    }
}