“…Due to the availability of huge amount of labeled data, and ability to work in a decentralized fashion, these techniques can be utilized for users' privacy preservation with enhanced usefulness. The heterogeneous federated transfer learning (HFTL) framework [298], privacy-preserving deep learning (PPDL) technique [299], deep transfer learning (DTL) method [300], adaptive privacy preserving federated learning (APPFL) method [301], block-chain-enable privacy preserving (BCEPP) architectures [302], [303], secure collaborative few-shot learning (SCFSL) framework [304], searchable encryption (SE) methods leveraging ciphertext-policy attribute-based encryption (CP-ABE) [305], [306], data resource protection solution leveraging smart contracts [307], improving cyber security solutions utilizing AI's potential [308], and computational intelligence based methods for information security [309], to name a few have already been used in practical applications related to the PPDP. Hence, devising robust and lightweight techniques which involve less parameters and can co-work with the traditional anonymization approaches to scale up privacy preservation with enhanced data utility is a promising area of research for the future.…”