Opportunities and Challenges of Privacy-Preserving Machine Learning Using Functional Encryption

 

Opportunities and Challenges of Privacy-Preserving Machine Learning Using Functional Encryption

Author: Brătescu Andrei-Ștefan

Machine learning is a powerful tool for extracting insights and making predictions from data. However, the use of machine learning algorithms requires access to large amounts of data, which raises privacy concerns.

One major problem:

In many cases, data owners are reluctant to share their data due to concerns about data privacy, intellectual property, or regulatory compliance.

Proposed solution:

Functional encryption is a promising approach for addressing these concerns. Functional encryption allows data owners to encrypt their data and provide access to specific functions or computations without revealing the underlying data. This allows machine learning algorithms to be trained on encrypted data, preserving the privacy of the data owners.

 

In this recent paper, the authors provide a comprehensive survey of the state-of-the-art in privacy-preserving machine learning using functional encryption.

Inner product functional encryption (IPFE) and quadratic functional encryption (QFE) are both types of functional encryption that can be used for privacy-preserving machine learning. IPFE is well-suited for computations that involve dot products and linear combinations of vectors, while QFE is designed for computations that involve quadratic functions. Both techniques enable data owners to encrypt their data and models, while still allowing authorized parties to perform machine learning computations on the encrypted data. The use of IPFE and QFE can help protect the privacy of sensitive data, while still allowing for useful insights to be gleaned from that data. Comparison and results can be observed in th

 

 

Conclusion:

Despite the promise of functional encryption for privacy-preserving machine learning, there are also significant challenges that must be overcome. One of the main challenges is the computational overhead of encryption and decryption operations, which can be prohibitively expensive for large-scale machine learning applications. Another challenge is the difficulty of ensuring the security and correctness of the various encryption and decryption operations, which can be vulnerable to attacks and errors.

Overall, the paper provides a comprehensive overview of the opportunities and challenges of using functional encryption for privacy-preserving machine learning. As machine learning continues to play an increasingly important role in various domains, it is likely that functional encryption will become an increasingly important tool for protecting the privacy of sensitive data.

Source:

https://arxiv.org/pdf/2204.05136.pdf