DOI: 10.5281/zenodo.18655966
title: An Empirical Benchmark Dataset for Paillier-Based Privacy-Preserving   REST API Gateways

description: Overview

homomorphic_request is a comprehensive benchmark dataset designed to evaluate the performance and reliability of Paillier-based homomorphic encryption in privacy-preserving REST API gateway architectures under realistic operational conditions. The dataset contains detailed telemetry from controlled experiments that simulate production workloads, including concurrency patterns, batch processing, fault injection, and retry mechanisms.

Motivation

Existing benchmarks for homomorphic encryption systems typically focus on isolated cryptographic operations in controlled environments with minimal concurrency and idealized network conditions. However, real-world deployments face challenges such as variable load patterns, network instability, queuing delays, and cascading failures. This dataset bridges that gap by providing empirical measurements from a realistic system under stress.

Dataset Description

Experimental Design

The dataset captures telemetry from multiple experimental suites targeting different operational aspects:



Concurrency scaling: Progressive increases in concurrent request load to identify bottlenecks and throughput limits

Batch size variation: Impact of different batch sizes on latency, throughput, and resource utilization

Homomorphic operation overhead: Isolated measurements of encryption/decryption and homomorphic computation costs

Burst traffic patterns: System behavior under sudden traffic spikes and high-throughput scenarios

Fault injection: Controlled introduction of network failures, timeouts, and service degradation with accompanying retry logic


Per-Request Telemetry

Each request record includes fine-grained timing breakdowns:



Client-side encryption latency

Full HTTP round-trip time

Gateway processing overhead

Queue waiting time

Homomorphic computation duration

Payload sizes (encrypted and plaintext)

Success/failure status

Failure classification (network, timeout, computation error, validation failure)

Retry count and backoff delays

Correctness validation: sampled decrypted results compared against expected plaintext computations


Metadata and Reproducibility

The dataset includes:



Run-level metadata: Git commit hash, configuration parameters, hardware specifications, software versions

Summary statistics: Per-experiment aggregations including percentile latencies, error rates, throughput metrics

Pre-aggregated comparison tables: Side-by-side performance comparisons across different configurations


All experiments are fully reproducible using the open-source Go-based benchmarking harness (included), which uses fixed seed values, deterministic scheduling jitter, and reproducible failure injection patterns.

Data Format

The dataset is available in two formats for broad compatibility:



CSV: Easily imported into pandas, R, Excel, or database systems

JSONL: Structured format suitable for streaming processing, NoSQL databases, and ML pipelines


Applications

This dataset has been used for:



Tail latency prediction and modeling

Failure mode classification and root cause analysis

Anomaly detection in encrypted computation workflows

Reliability and availability modeling

Performance optimization of homomorphic encryption gateways

Comparative evaluation of different cryptographic schemes and implementations


Target Audience

The dataset is intended for researchers and practitioners in:



Applied cryptography

Privacy-preserving systems and secure computation

Systems performance engineering

Distributed systems reliability

Network security and secure middleboxes

Machine learning for systems (performance prediction, anomaly detection)


Citation and Availability

The dataset and associated benchmarking tools are open-source and publicly available. Researchers using this dataset in publications are encouraged to cite it to enable reproducibility and comparison across studies.



Keywords: homomorphic encryption, Paillier cryptosystem, privacy-preserving computation, REST API, performance benchmarking, fault injection, distributed systems, secure computation