Complexity

The “complexity” in the proof-of-work mechanism refers to the level of computational effort required by a client to solve the challenge presented by the server. The complexity is controlled by adjusting the maximum value of the random number generated by the server during challenge creation.

The complexity setting serves as a measure to balance security against automated abuse/spam and user experience. By controlling the range of the randomly generated number, the system adjusts the difficulty of the computational task required from the client.

Note

The widget always starts the computational loop at 0 and does not support negative numbers.

Complexity test

Adjust the number of iterations and test the computational complexity on your device.

SHA-256 SHA-384 SHA-512 1 worker 2 workers 3 workers 4 workers 5 workers 6 workers 7 workers 8 workers 9 workers 10 workers 11 workers 12 workers 13 workers 14 workers 15 workers 16 workers Use Web Worker

Iterations: 0

Run test

Adjusting complexity

The complexity can be adjusted by modifying the minimum and maximum values of the randomly generated secret number on the server side. A lower maximum value creates a less complex challenge, requiring fewer computational iterations for the client to find a matching solution. Conversely, a higher maximum value increases the complexity, demanding more computational resources and time from the client.

Performance Benchmarks

These benchmarks illustrate the time required for different devices to solve an ALTCHA challenge. The table below shows the number of computations (operations) each device can perform per second and the corresponding time to solve a challenge with a complexity of 100,000. A higher number of operations per second indicates better performance.

Device	Performance	Time to Solve
MacBook Pro M3-Pro (2023)	3 ops/s	0.33 sec
iPhone 12 mini (2020)	1.2 ops/s	0.83 sec
AWS EC2 (c6a.xlarge)	1 ops/s	1 sec
Samsung Galaxy A14 (2023)	0.4 ops/s	2.5 sec
AWS Lambda (1GB)	0.12 ops/s	8 sec

Considerations

• Performance impact

  Higher complexity may significantly increase the computational load on client devices, potentially impacting user experience.

• Low-end versus high-end devices

  What takes less than a second on a new iPhone, might take 30 seconds on a low-end Android phone. Test with physical devices typical for your users.

• Security trade-offs

  Lower complexity might reduce security against automated attacks but can enhance user accessibility.

Recommended practices

• Dynamic complexity

  Implement a dynamic system that adapts complexity based on server load or user behavior, ensuring a balance between security and usability.

• Testing and optimization

  Conduct thorough testing to determine an optimal complexity level that prevents abuse without excessively burdening legitimate users.