On the Relationship between Model Quantization and Model Inversion Attacks
The paper investigates how reducing model precision via quantization affects model inversion attacks. It uses information theory to bound changes in prediction information and representation geometry caused by quantization. The authors propose a privacy-aware mixed-precision post-training quantization method that balances predictive accuracy with resistance to these inversion attacks.