Build a PQC-Secured Communication Channel
The advent of quantum computers poses a significant threat to current cryptographic standards. This challenge focuses on building a prototype for a secure, post-quantum resilient communication channel. Participants will implement key exchange and digital signature mechanisms using a selected Post-Quantum Cryptography (PQC) algorithm (e.g., Kyber for KEM, Dilithium for signatures). The task involves integrating a chosen PQC library, demonstrating a secure handshake, and evaluating the performance overhead. To aid in algorithm selection and parameter tuning, participants are encouraged to leverage `Optuna` for hyperparameter optimization of PQC implementation parameters (e.g., security levels, speed/size trade-offs) and `DeepSeek-R1` for generating or analyzing efficient C/Python bindings for PQC primitives or even for understanding the underlying math. The solution should emphasize practical implementation, demonstrating the feasibility of PQC in real-world scenarios, and providing a foundation for future fault-tolerant quantum security applications.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement a prototype PQC-secured communication channel, optimizing performance with Optuna and leveraging DeepSeek-R1 for PQC primitive analysis and code generation.
How work is evaluated
The evaluation will assess the correctness, security, performance, and the effective integration of PQC algorithms, Optuna, and DeepSeek-R1. Participants are expected to provide a well-documented and functional client-server PQC communication prototype.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Design and demonstrate a secure, PQC-protected communication handshake.
Understand the principles and challenges of Post-Quantum Cryptography (PQC).
Integrate `DeepSeek-R1` as a tool for PQC code analysis or generation.
Implement PQC algorithms for key encapsulation and digital signatures using a modern library.
Utilize `Optuna` to optimize performance/security parameters of PQC implementations.
Reference links and supporting material
Sample data for 3 tasks
Challenge setup
The evaluation will assess the correctness, security, performance, and the effective integration of PQC algorithms, Optuna, and DeepSeek-R1. Participants are expected to provide a well-documented and functional client...
Challenge input
Client and server Python scripts, configuration for PQC algorithms
Agent execution
No agent tool is listed for this challenge.
Evaluated output
Log indicating successful key exchange and message verification, performance metrics
- Client and server successfully establish a secure connection using PQC.
- The Optuna optimization script runs without errors and produces a valid result.
- Clear evidence of DeepSeek-R1's use for analysis or code generation.
- Average Handshake Latency (Ms) target: 100
- Docker execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Client and server successfully establish a secure connection using PQC.
- The Optuna optimization script runs without errors and produces a valid result.
- Clear evidence of DeepSeek-R1's use for analysis or code generation.
Metrics
- Average Handshake Latency (ms) · Target 100
- Signature Verification Success Rate · Target 100
- Optuna Optimization Impact (%) · Target 10
Workflow tasks
- Task 1
Input: Client and server Python scripts, configuration for PQC algorithms
Output: Log indicating successful key exchange and message verification, performance metrics
Execution harness: Docker
- Task 2
Input: Python script for Optuna study, Optuna database/results file
Output: JSON report detailing best parameters, objective values, and search space
Execution harness: Python
- Task 3
Input: Generated code snippets, analysis reports, or explanations from DeepSeek-R1
Output: JSON report summarizing DeepSeek-R1 contributions and their impact
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.