telemetry-kit Documentation

Differential privacy, zero-knowledge analytics, and encrypted user segments for maximum privacy protection

Overview

telemetry-kit v0.4.0+ includes cutting-edge privacy technologies that protect user data even when publishing aggregated analytics. These features go beyond basic anonymization to provide mathematically provable privacy guarantees.

These features are planned for v0.4.0 (Q2 2025) and are not yet implemented. This documentation serves as a design specification and preview.

Advanced Privacy Technologies:

Differential Privacy - Add calibrated noise to aggregations to prevent individual identification
Zero-Knowledge Analytics - Analyze trends without accessing individual event data
Encrypted User Segments - Group users while keeping identities encrypted end-to-end

Differential Privacy for Aggregations

What Is Differential Privacy?

Differential privacy is a mathematical framework that protects individual privacy in aggregate data by adding carefully calibrated noise. It provides a provable guarantee that no one can determine if a specific individual's data was included in a dataset.

The Problem

Consider this scenario:

// Your analytics dashboard shows:
Feature A: 10,000 users
Feature B: 8,500 users
Feature C: 1 user  // ⚠️ Privacy leak!

If someone knows Alice is your only user interested in Feature C, they can deduce that Alice used Feature C just by looking at the aggregate count.

The Solution

Differential privacy adds Laplace noise to each count:

// With differential privacy (epsilon = 0.1):
Feature A: 10,003 users  // Real: 10,000 + noise: +3
Feature B: 8,497 users   // Real: 8,500 + noise: -3
Feature C: 4 users       // Real: 1 + noise: +3 ✅ Privacy protected!

Now it's impossible to tell if Alice actually used Feature C or if the count is purely noise.

API Design (v0.4.0)

use telemetry_kit::prelude::*;
use telemetry_kit::analytics::DifferentialPrivacy;
 
#[tokio::main]
async fn main() -> Result<()> {
    let telemetry = TelemetryKit::builder()
        .service_name("my-app")?
        .with_differential_privacy(true)  // Enable DP
        .build()?;
 
    // Query analytics with differential privacy
    let stats = telemetry
        .analytics()
        .feature_usage()
        .with_differential_privacy()  // Apply DP to results
        .query()
        .await?;
 
    for (feature, count) in stats {
        println!("{}: {} users", feature, count);
        // Counts are noised for privacy
    }
 
    Ok(())
}

How It Works

Compute True Aggregate

let true_count = database
    .query("SELECT COUNT(*) FROM events WHERE feature = 'login'")
    .await?;
// Result: 1

Generate Laplace Noise

use rand_distr::{Distribution, Laplace};
 
let sensitivity = 1.0;  // Max change from one user
let epsilon = 0.1;      // Privacy parameter
let scale = sensitivity / epsilon;
 
let laplace = Laplace::new(0.0, scale)?;
let noise = laplace.sample(&mut rng);
// noise ≈ +3.2

Add Noise to Result

let noised_count = (true_count as f64 + noise).max(0.0) as u64;
// 1 + 3.2 = 4.2 → 4 (rounded)
 
// Return to user
println!("Login feature: {} users", noised_count);
// Output: "Login feature: 4 users" ✅ Privacy protected

Privacy-Accuracy Tradeoff

Epsilon (ε) controls the privacy-accuracy tradeoff:

Lower ε = More privacy (more noise, less accuracy)
Higher ε = Less privacy (less noise, more accuracy)

Epsilon	Privacy Level	Noise Magnitude	Use Case
0.01	Maximum	Very high	Medical records, financial data
0.1	Strong	High	Personal usage analytics
1.0	Moderate	Medium	General analytics (recommended)
10.0	Weak	Low	Public datasets

Mathematical Guarantee

Differential privacy provides ε-differential privacy, meaning:

Pr[M(D) ∈ S] ≤ e^ε × Pr[M(D') ∈ S]

Where:

M = Mechanism (your query with noise)
D = Dataset with Alice's data
D' = Dataset without Alice's data
ε = Privacy parameter

Translation: An attacker can't determine if Alice's data is in the dataset with confidence > e^ε.

Properties

Composability - Multiple DP queries degrade privacy predictably
Post-Processing - Transforming DP results preserves privacy
Group Privacy - Protects groups of k users with ε×k guarantee
Plausible Deniability - Alice can deny participation

Example: Feature Usage Analytics

use telemetry_kit::analytics::*;
 
// Without differential privacy ⚠️
let stats = telemetry.analytics()
    .feature_usage()
    .group_by_feature()
    .query()
    .await?;
 
println!("Export PDF: 1 user");  // Identifies individual!
 
// With differential privacy ✅
let stats = telemetry.analytics()
    .feature_usage()
    .group_by_feature()
    .with_differential_privacy()
    .epsilon(0.1)
    .query()
    .await?;
 
println!("Export PDF: {} users", stats.get("export_pdf")?);
// Output: "Export PDF: 4 users" (1 + noise)
// Can't tell if anyone actually used it!

Zero-Knowledge Analytics

What Is Zero-Knowledge Analytics?

Zero-knowledge analytics allows you to compute aggregate trends without ever seeing individual user data. The server analyzes events in an encrypted form and only reveals statistical summaries.

The Problem

Traditional analytics requires the server to see individual events:

// Server sees individual events ⚠️
{
  "user_id": "user_123",
  "event": "login",
  "timestamp": "2025-01-15T10:30:00Z"
}
 
// Even with hashed IDs, server can:
// - Link events to the same user
// - Build usage profiles
// - Identify outliers

The Solution

Homomorphic encryption allows computation on encrypted data:

// Server receives encrypted events ✅
{
  "encrypted_data": "a8f3k2j9...",  // Can't decrypt
  "proof": "zk_proof_data..."       // Proves validity
}
 
// Server computes on encrypted data
let encrypted_count = sum(encrypted_events);
 
// Client decrypts result
let true_count = decrypt(encrypted_count);
// Server never saw individual events!

API Design (v0.4.0)

use telemetry_kit::prelude::*;
use telemetry_kit::privacy::ZeroKnowledge;
 
#[tokio::main]
async fn main() -> Result<()> {
    // Generate client encryption keys
    let zk_keys = ZeroKnowledge::generate_keys()?;
 
    let telemetry = TelemetryKit::builder()
        .service_name("my-app")?
        .with_zero_knowledge(zk_keys.public_key)
        .build()?;
 
    // Events are encrypted before sending
    telemetry.track_command("login", |e| e.success(true)).await?;
    // Server receives encrypted event ✅
 
    // Query analytics (client-side decryption)
    let stats = telemetry
        .analytics()
        .decrypt_with(zk_keys.private_key)
        .feature_usage()
        .query()
        .await?;
 
    println!("Login count: {}", stats.get("login")?);
    // Server computed this on encrypted data!
 
    Ok(())
}

How It Works

Client Encrypts Event

use telemetry_kit::crypto::homomorphic;
 
let event = Event::new("login", true);
 
// Encrypt with public key
let encrypted = homomorphic::encrypt(
    &public_key,
    &event.to_bytes()?
)?;
 
// Generate zero-knowledge proof
let proof = homomorphic::prove(
    &event,
    &encrypted,
    &public_key
)?;
 
// Send to server
client.send(encrypted, proof).await?;

Server Verifies and Aggregates

// Verify ZK proof (without decrypting)
if !homomorphic::verify(&encrypted, &proof, &public_key) {
    return Err("Invalid proof");
}
 
// Aggregate encrypted events
let encrypted_sum = encrypted_events
    .iter()
    .fold(EncryptedValue::zero(), |acc, event| {
        homomorphic::add(&acc, &event)  // Addition on ciphertext!
    });
 
// Return encrypted aggregate
encrypted_sum

Client Decrypts Result

// Receive encrypted aggregate from server
let encrypted_count = server.query_analytics().await?;
 
// Decrypt with private key
let count = homomorphic::decrypt(
    &private_key,
    &encrypted_count
)?;
 
println!("Total logins: {}", count);
// Server never knew this number!

Supported Operations

Homomorphic encryption supports limited operations:

Operation	Supported	Example
Addition	✅ Yes	`SUM(events)`
Subtraction	✅ Yes	`COUNT(success) - COUNT(failure)`
Multiplication (limited)	⚠️ Partial	Quadratic only
Division	❌ No	Use client-side
Comparison	❌ No	Use client-side

Performance Considerations

Performance Impact: Zero-knowledge analytics is computationally expensive:

Encryption: ~10ms per event
Server aggregation: 2-5x slower than plaintext
Decryption: ~5ms per result

Recommended for privacy-critical use cases only.

Encrypted User Segments

What Are Encrypted User Segments?

Encrypted user segments allow you to group users by behavior while keeping their identities encrypted. You can analyze "users who did X" without ever knowing who those users are.

The Problem

Traditional segmentation exposes user groups:

// Unencrypted segments ⚠️
Segment: "Power Users" = [user_123, user_456, user_789]
 
// Server (and attackers) can:
// - See who's in each segment
// - Track users across segments
// - Correlate with external data

The Solution

Secure multi-party computation and homomorphic encryption allow segment creation without revealing membership:

// Encrypted segments ✅
Segment: "Power Users" = [enc_a8f3, enc_k2j9, enc_m7n4]
 
// Server knows:
// - Segment size: 3 users
// - Aggregate stats: avg session 45min
 
// Server doesn't know:
// - Who is in the segment
// - Individual user behaviors
// - Cross-segment correlation

API Design (v0.4.0)

use telemetry_kit::segments::*;
 
// Define segment criteria (client-side)
let power_users = SegmentBuilder::new("power_users")
    .criterion(|user| {
        user.event_count > 100
            && user.last_active < 7.days_ago()
    })
    .encrypted(true)  // Encrypt membership
    .build()?;
 
// Server computes segment without seeing members
let segment = telemetry
    .segments()
    .create(power_users)
    .await?;
 
println!("Segment size: {}", segment.size());
// Server knows size, not members

How It Works

Client Encrypts User ID

use telemetry_kit::crypto::paillier;
 
let user_id = telemetry.user_id();
 
// Encrypt user ID with homomorphic encryption
let encrypted_id = paillier::encrypt(
    &public_key,
    user_id.as_bytes()
)?;
 
// Send to server for segment evaluation
client.send_for_segmentation(encrypted_id).await?;

Server Evaluates Criteria (Encrypted)

// Server evaluates segment criteria on encrypted data
let meets_criteria = encrypted_id
    .event_count > threshold_encrypted
    && encrypted_id.last_active < cutoff_encrypted;
 
// Result is encrypted boolean
// Server doesn't know if this specific user qualifies
 
// Add to segment if criteria met
if meets_criteria {
    segment.add_encrypted_member(encrypted_id);
}

Client Queries Membership

// Generate ZK proof of membership
let proof = segment.generate_membership_proof(
    &user_id,
    &private_key
)?;
 
// Server verifies proof
if server.verify_membership(&proof) {
    // User is in segment ✅
    // Server verified without learning identity
}

Use Cases

1. A/B Testing (Privacy-Preserving)

// Create test segment without revealing members
let test_group = SegmentBuilder::new("feature_x_test")
    .criterion(|user| user.id_hash % 2 == 0)  // 50% split
    .encrypted(true)
    .build()?;
 
// Show feature only to test group
if telemetry.segments().get("feature_x_test").am_i_member().await? {
    show_feature_x();
}
 
// Server tracks conversion rates per segment
// without knowing who's in which group

2. User Cohorts

// Define cohorts by behavior
let cohorts = vec![
    Segment::new("new_users").criterion(|u| u.days_since_signup < 7),
    Segment::new("active_users").criterion(|u| u.events_7d > 10),
    Segment::new("churned_users").criterion(|u| u.days_since_active > 30),
];
 
// All memberships encrypted
for cohort in cohorts {
    let stats = telemetry.segments().create(cohort).analytics().await?;
    println!("{}: {} users", stats.name, stats.size);
}

3. Premium User Tracking

// Track premium users without exposing list
let premium = SegmentBuilder::new("premium")
    .criterion(|user| user.has_subscription)
    .encrypted(true)
    .build()?;
 
// Server knows aggregate premium user metrics
// but not individual premium users

Implementation Roadmap

v0.4.0 (Q2 2025) - Differential Privacy

v0.5.0 (Q3 2025) - Zero-Knowledge Analytics

v0.6.0 (Q4 2025) - Encrypted Segments

Segment Engine

Encrypted segment membership
Secure multi-party computation for criteria evaluation
ZK membership proofs
Segment analytics on encrypted data

Advanced Features

Dynamic segment updates
Hierarchical segments
Cross-segment analytics (encrypted)
Performance optimizations

Performance Benchmarks (Projected)

These are estimated performance characteristics based on similar implementations. Actual performance will be measured and documented when features are implemented.

Feature	Operation	Overhead	Throughput
Differential Privacy	Add noise to aggregate	~0.1ms	10,000 queries/sec
Zero-Knowledge	Encrypt event	~10ms	100 events/sec
Zero-Knowledge	Aggregate (encrypted)	2-5x slower	20-50 queries/sec
Encrypted Segments	Membership test	~5ms	200 tests/sec
Encrypted Segments	Segment creation	~50ms	20 segments/sec

Security Considerations

Differential Privacy

Epsilon Selection - Lower is more private but less accurate
Composition - Multiple queries degrade privacy (track budget)
Auxiliary Information - DP doesn't protect against external data correlation
Post-Processing - Always safe (doesn't degrade privacy)

Zero-Knowledge Analytics

Key Management - Private keys must be protected
Proof Verification - Always verify proofs server-side
Computational Cost - ZK is expensive (use selectively)
Quantum Resistance - Current schemes not quantum-safe

Encrypted Segments

Segment Size Leakage - Size is revealed (add DP noise if sensitive)
Membership Inference - Use ZK proofs to prevent leakage
Criteria Complexity - Complex criteria harder to evaluate encrypted
Cache Timing Attacks - Implement constant-time operations

Best Practices

1. Choose the Right Privacy Level

// Low-sensitivity data (public analytics)
.with_differential_privacy()
.epsilon(1.0)
 
// Medium-sensitivity (user behavior)
.with_differential_privacy()
.epsilon(0.1)
 
// High-sensitivity (medical, financial)
.with_zero_knowledge(keys)

2. Track Privacy Budget

let budget = PrivacyBudget::new(1.0);  // Total epsilon
 
// Query 1
telemetry.analytics()
    .with_dp(0.3)  // Consumes 0.3
    .query().await?;
 
// Query 2
telemetry.analytics()
    .with_dp(0.5)  // Consumes 0.5
    .query().await?;
 
// Budget remaining: 0.2
// Enforce limits to prevent privacy degradation

3. Combine Techniques

// Maximum privacy: DP + ZK + Encrypted Segments
let telemetry = TelemetryKit::builder()
    .service_name("ultra-private-app")?
    .with_zero_knowledge(keys)           // Encrypt events
    .with_differential_privacy(true)      // Noise aggregates
    .with_encrypted_segments(true)        // Encrypted cohorts
    .build()?;

4. Document Privacy Guarantees

// In your privacy policy:
// "We use ε-differential privacy with ε=0.1 for all analytics queries.
//  This provides a mathematical guarantee that individual user data
//  cannot be inferred from aggregate statistics."

Get Involved

These features are complex and require careful design. We'd love your input:

Have expertise in cryptography or differential privacy? We'd especially appreciate your review and contributions!

Advanced Privacy Features

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