This repo provides the code for 
"Privacy-preserving Voice Analysis via Disentangled Representations" paper.

Voice User Interfaces (VUIs) are increasingly popular and built into smartphones, home assistants, and Internet of Things (IoT) devices. Despite offering an always-on convenient user experience, VUIs raise new security and privacy concerns for their users. In this paper, we focus on attribute inference attacks in the speech domain, As shown in the figure below, demonstrating the potential for an attacker to accurately infer a target user's sensitive and private attributes (e.g. their emotion, sex, or health status) from deep acoustic models. To defend against this class of attacks, we design, implement, and evaluate a user-configurable, privacy-aware framework for optimizing speech-related data sharing mechanisms.

• Ensure you have Python 3 and PyTorch 1.4 or greater
• Install NVIDIA/apex for mixed precision training
• Install all package dependencies

Details under **Training Folder**

To generate different output, first specify the following options:

• privacy_preference = [Low, Moderate, High]
• output_type = [Recording, Embedding]

Then,

• raw_dir: path to raw wavs files
• generated_dir: path to generated wavs files
• checkpoint: path to checkpoint model
• filtring_list: JSON file contains list of (path to raw wav, target/same speaker, generated file name)

Example:

python DDF.py privacy_preference=[?] output_type=[?] checkpoint=[?] 
              raw_dir=[?] generated_dir=[?] filtring_list=[?] dataset=[LibriSpeech]

If you find this work useful, please cite us.
@misc{aloufi2020privacypreserving,
    title={Privacy-preserving Voice Analysis via Disentangled Representations},
    author={Ranya Aloufi and Hamed Haddadi and David Boyle},
    year={2020},
    eprint={2007.15064},
    archivePrefix={arXiv},
    primaryClass={eess.AS}
}

This project welcomes contributions and suggestions. 
Please contact **ra6018@imperial.ac.uk** with any additional questions or comments.