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DiffPriv
DiffPriv is a differential privacy framework for real time data streaming written in Rust. Supporting k-anonymity, (c,l)-diversity and ε-differential privacy. The framework is based on the Preserving Differential Privacy and Utility of Non-stationary Data Streams paper, with various improvements implemented.
This library is the result of my master thesis: Differential privacy in large scale data streaming. It has been developer during an internship at STRM Privacy
Using the anonymizer
An example of using the anonymizer can be seen below
use csv::Reader;
use diff_priv::anonymization::microagg_anonymizer::MicroaggAnonymizer;
use diff_priv::noise::laplace::laplace_noiser::LaplaceNoiser;
use diff_priv::test::adult::Adult;
use diff_priv::test::dummy_publisher::DummyPublisher;
// we initialize our noiser that implements the `Noiser` trait
let noiser = LaplaceNoiser::new(0.1, 3, 0.1);
// we initialize a publisher that implements the `Publisher` trait
let publisher = DummyPublisher::default();
// we create the anonymizer with the desired parameters
// k: 2 | k_max: 10 | c: 2 | l: 7 | diff_thres: 0.1 | delta: 10 | buff_size: 5
let mut anonymizer: MicroaggAnonymizer<LaplaceNoiser, Adult, DummyPublisher> =
MicroaggAnonymizer::new(2, 10, 2, 7, 0.1, 10, 5, publisher, noiser);
// load CSV file representing an Adult
let mut file = Reader::from_path("datasets/Adult_1_numeric_only_class_50K.csv").unwrap();
for line in file.deserialize() {
let row_result = line;
// when we call for `anonymizer()` the anonymizer will
// automatically publish to the given backend when the given
// privacy parameter conditions are met
match row_result {
Ok(row) => anonymizer.anonymize(row),
Err(e) => panic!("{}", e)
}
}
// publish remaining data tuples to the given publisher
// in this case a `DummyPublisher`
anonymizer
.cluster_set
.into_iter()
.for_each(|(_, mut cluster)| {
cluster.publish_all(&mut anonymizer.publisher, &mut anonymizer.analysers)
});Implementing Anonymizable trait to anonymize new data
By implementing the Anonymizable trait on any type of datastructure, DiffPriv will know how to anonymize it.
The following QIs types are implemented
/// value, min_value, max_value, weight of attribute
pub type IntervalType = (
QuasiIdentifierType,
QuasiIdentifierType,
QuasiIdentifierType,
usize,
);
/// rank, max_rank, weight of attribute
pub type OrdinalType = (i32, i32, usize);
/// value, max value, weight of attribute
pub type NominalType = (i32, i32, usize);An example implementation of the Anonymizable trait can be seen below
use std::time::{SystemTime, UNIX_EPOCH};
use serde::{Serialize, Deserialize};
use bimap::BiMap;
use lazy_static::lazy_static;
use uuid::Uuid;
use diff_priv::data_manipulation::anonymizable::{
Anonymizable, QuasiIdentifierType, QuasiIdentifierTypes, SensitiveAttribute,
};
lazy_static! {
static ref CLASS_BIMAP: BiMap<&'static str, i32> =
BiMap::from_iter(vec![("<=50K", 0), (">50K", 1),]);
}
// This is the datastructure that we are going to anonymize
#[derive(Debug, Serialize, Clone, Deserialize)]
pub struct Adult {
timestamp: i32,
age: i32,
capital_gain: i32,
capital_loss: i32,
class: String,
#[serde(skip_deserializing, default = "default_time")]
time_generated: SystemTime,
}
fn default_time() -> SystemTime {
SystemTime::now()
}
impl Default for Adult {
fn default() -> Self {
Self {
timestamp: 0,
age: 0,
capital_gain: 0,
capital_loss: 0,
class: "".to_string(),
time_generated: SystemTime::now(),
}
}
}
impl Adult {
// here we extract an Interval QI from the `age` attribute
fn get_age_qi(&self) -> QuasiIdentifierTypes {
QuasiIdentifierTypes::Interval((
QuasiIdentifierType::Integer(self.age),
QuasiIdentifierType::Integer(1),
QuasiIdentifierType::Integer(100),
1,
))
}
// here we extract an Interval QI from the `capital_gain` attribute
fn get_capital_gain_qi(&self) -> QuasiIdentifierTypes {
QuasiIdentifierTypes::Interval((
QuasiIdentifierType::Integer(self.capital_gain),
QuasiIdentifierType::Integer(0),
QuasiIdentifierType::Integer(100000),
1,
))
}
// here we extract an Interval QI from the `capital_loss` attribute
fn get_capital_loss_qi(&self) -> QuasiIdentifierTypes {
QuasiIdentifierTypes::Interval((
QuasiIdentifierType::Integer(self.capital_loss),
QuasiIdentifierType::Integer(0),
QuasiIdentifierType::Integer(5000),
1,
))
}
}
// Here we implement the `Anonymizable` trait
impl Anonymizable for Adult {
// We extract the QIs from the datastructure and return a `vec` of QIs
fn quasi_identifiers(&self) -> Vec<QuasiIdentifierTypes> {
let age = self.get_age_qi();
let capital_gain = self.get_capital_gain_qi();
let capital_loss = self.get_capital_loss_qi();
vec![
age,
capital_gain,
capital_loss,
]
}
// We update the datastructures QIs with a `vec` of QIs. The `vec` needs to be
// popped in the same order that the QIs are extracted with the `quasi_identifiers`
// function
fn update_quasi_identifiers(&self, mut qi: Vec<QuasiIdentifierTypes>) -> Self {
if let (
QuasiIdentifierType::Integer(capital_loss),
QuasiIdentifierType::Integer(capital_gain),
QuasiIdentifierType::Integer(age),
) = (
qi.pop().unwrap().extract_value(),
qi.pop().unwrap().extract_value(),
qi.pop().unwrap().extract_value(),
) {
Self {
timestamp: self.timestamp,
age,
capital_gain,
capital_loss,
class: self.class.to_owned(),
time_generated: self.time_generated,
}
} else {
panic!("Couldn't Adult with QI's")
}
}
// We extract the sensative attribute from the datastructure
fn sensitive_value(&self) -> SensitiveAttribute {
SensitiveAttribute::String(self.class.to_owned())
}
// We return a vector of strings containing the String version of the QIs
// Used for printing to CSVs
fn extract_string_values(&self, uuid: Uuid, dr: f64) -> Vec<String> {
vec![
uuid.to_string(),
dr.to_string(),
self.timestamp.to_string(),
self.age.to_string(),
self.capital_gain.to_string(),
self.capital_loss.to_string(),
self.class.to_owned(),
]
}
fn get_timestamp(&self) -> SystemTime {
self.time_generated
}
}The Publisher trait
To publish an anonymized struct to a desired backend we use the Publisher trait.
DiffPriv also support exporting to an Apache Kafka topic. This can be seen in publishing directory.
An example publisher for CSVs can be seen here: CsvPublisher.
To implement a custom publishing backend one can use the Publisher trait.
The Noiser trait
DiffPriv support Laplace noise for ε-differential privacy. The noiser supports 2 different kind of noise: one for numerical values and one for categorical. To implement a custom implementation of ε-differential privacy noise, one can use the Noiser trait.