Interpretable Encrypted Searchable Neural Networks. In cloud security, traditional searchable encryption (SE) requires high computation and communication overhead for dynamic search and update. The clever combination of machine learning (ML) and SE may be a new way to solve this problem. This paper proposes interpretable encrypted searchable neural networks (IESNN) to explore probabilistic query, balanced index tree construction and automatic weight update in an encrypted cloud environment. In IESNN, probabilistic learning is used to obtain search ranking for searchable index, and probabilistic query is performed based on ciphertext index, which reduces the computational complexity of query significantly. Compared to traditional SE, it is proposed that adversarial learning and automatic weight update in response to useru0027s timely query of the latest data set without expensive communication overhead. The proposed IESNN performs better than the previous works, bringing the query complexity closer to O(log N) and introducing low overhead on computation and communication.

IVP: A Three Level ConfusionDiffusion Network Supporting Implicit Data Integrity We present a cryptographic construction called IVP and study its security properties. IVP is a three level confusiondiffusion network that supports confidentiality and data integrity without requiring any message expansion of the content, such as, for example, for the computation of a MAC. We demonstrate that IVP is in the recently proposed class of cryptographic constructions called xe2x80x98Random Oracles according to Observer functionsxe2x80x99 (RO2). These constructions support a new notion of data integrity called xe2x80x98implicitxe2x80x99 data integrity, which is based on the fact that user data usually demonstrate some patterns. If some ciphertext becomes corrupted, then the resulting plaintext no longer demonstrates such patterns. Thus, defense against data corruption attacks becomes possible by hardening the computation of ciphertext values, the plaintext of which demonstrates patterns. The encryption key is considered unknown.We show that IVP supports implicit data integrity and is secure in input perturbing and oracle replacing adversary models. The security of IVP is associated with a pattern which is frequently encountered among client and server data. This is the pattern of encountering 4 or more 16bit words being equal to each other in a set of 32 words. The cryptographic strength of IVP is 30.215 bits, which is sufficient for defending against online data corruption and content replay attacks. Computationally, IVP is much lighter than other authenticated encryption approaches requiring only two additional rounds of AES, beyond the AES standard encryption rounds in the critical path. These correspond to some minimal computation overhead.