Auditable Homomorphic-based Decentralized Collaborative AI with
  Attribute-based Differential Privacy
Lo-Yao Yeh Sheng-Po Tseng Chia-Hsun Lu Chih-Ya Shen
In recent years, the notion of federated learning (FL) has led to the new
paradigm of distributed artificial intelligence (AI) with privacy preservation.
However, most current FL systems suffer from data privacy issues due to the
requirement of a trusted third party. Although some previous works introduce
differential privacy to protect the data, however, it may also significantly
deteriorate the model performance. To address these issues, we propose a novel
decentralized collaborative AI framework, named Auditable Homomorphic-based
Decentralised Collaborative AI (AerisAI), to improve security with homomorphic
encryption and fine-grained differential privacy. Our proposed AerisAI directly
aggregates the encrypted parameters with a blockchain-based smart contract to
get rid of the need of a trusted third party. We also propose a brand-new
concept for eliminating the negative impacts of differential privacy for model
performance. Moreover, the proposed AerisAI also provides the broadcast-aware
group key management based on ciphertext-policy attribute-based encryption
(CPABE) to achieve fine-grained access control based on different service-level
agreements. We provide a formal theoretical analysis of the proposed AerisAI as
well as the functionality comparison with the other baselines. We also conduct
extensive experiments on real datasets to evaluate the proposed approach. The
experimental results indicate that our proposed AerisAI significantly
outperforms the other state-of-the-art baselines.