Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges

TL;DR: A novel approach, named splitfed (SFL), is presented that amalgamates the two approaches of federated learning and split learning eliminating their inherent drawbacks and offers better efficiency than SL by incorporating the parallel ML model update paradigm of FL.

TL;DR: The impact of edge computing on the development of IoT is introduced and why edge computing is more suitable for IoT than other computing paradigms are pointed out and some challenging and worthwhile research directions are proposed.

TL;DR: In this article, the authors proposed a cloud-based revocable identity-based proxy re-encryption (CR-IB-PRE) scheme that supports user revocation but also delegation of decryption rights.

TL;DR: The developed distributed approach has the scalability to partition both data and analysis computations into subset cloud computing nodes that can be run independently, which rapidly accelerates the performance of encrypted data processing while preserving a high level of analysis accuracy.

TL;DR: A new signature scheme is proposed, together with an implementation of the Diffie-Hellman key distribution scheme that achieves a public key cryptosystem that relies on the difficulty of computing discrete logarithms over finite fields.

TL;DR: A new trapdoor mechanism is proposed and three encryption schemes are derived : a trapdoor permutation and two homomorphic probabilistic encryption schemes computationally comparable to RSA, which are provably secure under appropriate assumptions in the standard model.

TL;DR: This work proposes a fully homomorphic encryption scheme that allows one to evaluate circuits over encrypted data without being able to decrypt, and describes a public key encryption scheme using ideal lattices that is almost bootstrappable.

TL;DR: In this article, the authors give a general impossibility result showing that a formalization of Dalenius' goal along the lines of semantic security cannot be achieved, and suggest a new measure, differential privacy, which, intuitively, captures the increased risk to one's privacy incurred by participating in a database.