Cryptography has always fascinated me – the delicate balance of art and science that underpins secure communication is nothing short of magical. With widely trusted systems like AES, RSA, and LUKS dominating the landscape, I embarked on an ambitious project: to design and implement my own encryption algorithm.
This blog post outlines my journey, the challenges I faced, and the lessons I learned while creating a custom encryption algorithm. Though it isn’t intended to replace industry-standard algorithms, this project deepened my understanding of cryptographic principles and the complexities of building secure systems.
The Vision
The goal was clear: to create a secure, efficient, and modular encryption system that could handle large-scale data, such as disk encryption, with performance and security akin to LUKS. This meant incorporating advanced features like key derivation, block cipher chaining, and sector-based encryption, while ensuring it could withstand known cryptographic attacks.
I also wanted to:
- Design the algorithm from the ground up, focusing on fundamental cryptographic principles.
- Support extensibility for adding features like multiple keys or recovery slots.
- Maintain simplicity and elegance without sacrificing robustness.
Building Blocks of the Algorithm
Designing an encryption system is like assembling a puzzle where each piece must fit perfectly. Here’s how I approached each component.
1. Key Derivation
The first challenge was transforming a human-readable passphrase into a strong, cryptographically secure key. I implemented a key derivation function (KDF) inspired by PBKDF2 and Argon2. It uses:
- A salt to ensure uniqueness, preventing attackers from using precomputed hash tables.
- Iterative hashing to make brute-force attacks computationally expensive.
I spent considerable time fine-tuning the number of iterations, balancing security with performance.
2. Encryption Core
The heart of the algorithm is its encryption core. I opted for a symmetric block cipher approach, similar to AES, with each data block encrypted independently. To avoid the pitfalls of simpler methods like ECB (Electronic Codebook), I implemented a chaining mode inspired by XTS (XEX-based Tweaked Codebook with Ciphertext Stealing).
This mode uses two keys:
- One to encrypt the data.
- Another to generate a unique tweak for each block, ensuring no two blocks are encrypted the same way.
3. Initialization Vectors
Initialization vectors (IVs) are crucial for adding randomness to encryption. For my algorithm, each block and sector gets a unique IV, computed from:
- The sector index.
- A global tweak value derived from the key.
This ensures even if a sector contains predictable data (like an empty filesystem), the ciphertext remains unique.
4. Metadata Header
To support recovery and flexibility, I designed a metadata header that stores:
- Cryptographic parameters, like the salt and the number of KDF iterations.
- Key slots for managing multiple keys, enabling features like recovery passphrases.
- Versioning information for future extensibility.
The header is securely encrypted to prevent tampering and includes an integrity check.
5. Sector-Based Encryption
Given my focus on disk-level encryption, I divided the data into fixed-size sectors. Each sector is encrypted independently using its IV, making random access efficient. This approach mirrors the design philosophy of LUKS, ensuring scalability and usability for large datasets.
The Implementation Process
Implementing the algorithm involved meticulous planning and iteration. Here’s how I structured the process:
Step 1: Research and Planning
I began by studying established encryption systems like AES, XTS, and LUKS to understand their strengths and weaknesses. This phase also involved deep dives into cryptographic principles like substitution-permutation networks, chaining modes, and key schedules.
Step 2: Prototyping
I started small, building a prototype that could encrypt and decrypt single blocks of data. This helped me validate the core logic before scaling up to full-sector encryption.
Step 3: Optimization
Performance is critical for encryption systems. I optimized the algorithm for speed by:
- Leveraging hardware acceleration (e.g., AES-NI) where possible.
- Implementing parallel encryption for multiple sectors.
- Minimizing memory usage to handle large datasets efficiently.
Step 4: Testing
Testing was perhaps the most challenging and rewarding phase. I conducted:
- Functional Tests: Ensuring encrypted data could be decrypted without loss.
- Security Tests: Simulating brute-force and known-plaintext attacks.
- Edge Case Handling: Encrypting data of varying sizes, misaligned sectors, and corrupted headers.
Challenges and Insights
Challenge 1: Balancing Security and Usability
Stronger encryption often means slower performance. Tuning the number of KDF iterations and choosing efficient chaining modes required striking a delicate balance.
Challenge 2: Preventing Metadata Leaks
The metadata header is a critical part of the algorithm, but it also poses a risk. Any leak in its design could expose key parameters to attackers. Encrypting the header and including integrity checks addressed this challenge.
Challenge 3: Handling Corruption
Disk encryption must handle data corruption gracefully. I designed the algorithm to ensure that corruption in one sector doesn’t propagate to others, preserving data integrity as much as possible.
Lessons Learned
1. Simplicity is Security
Every added feature introduces complexity, which can lead to vulnerabilities. Keeping the design modular and straightforward minimized potential attack vectors.
2. The Importance of Peer Review
Even the most well-thought-out designs can have blind spots. I gained a newfound appreciation for the rigorous peer review process that established algorithms undergo.
3. Performance is Key
Encryption systems operate in real-world environments where performance impacts usability. Efficient algorithms not only improve user experience but also reduce the energy footprint of encryption processes.