Chain-of-Blocks Assisted Secure Feature Selection, Federated Learning and Classifications in Cloud and Distributed Malicious Edge IoT Environments

We tackle the problem of secure and private feature selection by homomorphically evaluating features’ information gains over the encrypted data of horizontally partitioned private datasets owned by edge IoT (Internet of Things) devices. In the process, we use a powerful cloud server to do the bulk of the costly homomorphic encryption aggregations. We proceeded with secure and private federated learning (training) and Machine Learning (ML) classification over the selected features in the same environmental settings (context). In the process, the participants interact with each other under strict security, privacy, and efficiency requirements. To this end, to each participant’s interaction we provide confidentiality, integrity, and authenticity (CIA) by signing its hashed contents with the corresponding participant’s private key. We assure consistency among interactions by introducing timestamps and linking them with the hashed content(s) of the preceding interaction(s). Those linked blocks of hashed content(s) from each interaction of participants while running the protocols produce the so-called chain-of-blocks (COB) structure, which will be utilized to detect malicious edge IoT dataset owners, unauthorized participants, and network errors. The security of the proposed protocols is proven through rigorous mathematical modeling. Extensive experimental evaluations over benchmark datasets give an advantage to our secure protocols ranging from several times to orders of magnitudes w.r.t to the state of the art in terms of computation and communication costs, as well as security and privacy characteristics. Moreover, since the utilized underlying cryptographic techniques are resilient to quantum computer attacks, the proposed algorithms are applicable to the post-quantum world.