Short Introduction to This Paper
This paper introduces and explores the idea of data poisoning, a light-weight peer-architecture technique to inject faults into Python programs. This method requires very small modification to the original program, which facilitates evaluation of sensitivity of systems that are prototyped or modeled in Python. Actually this paper doesn't show much detail about the implementation, but the types of data poisoning it declares are very interesting.
Highlights of This Paper
- Data poisoning's symbolic expression
- Different types of data poisoning
- Types of data poisoning: deterministic effect poisoning, intermittent effect poisoning (need define the lifetime of poisoned data), infectious/non-infectious poisoning
Relevant Future Works
- Only doing data poisoning is not enough, we should analysis the system's behaviour under different types of perturbation