Huiyan Chen

Authenticated encryption with associated data (AEAD) provides confidentiality and integrity but need not commit a ciphertext to its key. We study this additional property for ACORN-128 v3 and Deoxys-I/II. Our baseline constructions use two distinct known keys and a common nonce while allowing different associated data (AD), and produce identical complete ciphertext–tag pairs. They directly witness failures of the commitment (CMT) notions for the key (CMT-1), decryption context (CMT-3), and complete encryption input (CMT-4), as well as the full-robustness notion for AEAD (AEAD-FROB) used here. For ACORN, we prove full rank of a 293-dimensional binary control matrix, guaranteeing state collisions for arbitrary initialized states; a byte-oriented implementation uses 37-byte controllable AD suffixes and can preserve a common AD prefix. For Deoxys, we instantiate the established AD-preimage technique, requiring two forward and one inverse tweakable-block-cipher calls for the empty-message Deoxys-I construction, and one forward and two inverse calls for Deoxys-II. We also derive nonempty-message constructions and verify two-key instances with both nonce and AD common by solving a binary selection system. Fixed-version reference implementations verify all reported collisions and both decryptions. These results concern commitment under the stated input control and do not establish attacks on conventional unknown-key AEAD security or on specific deployed protocols.

The idea of multi-keyword ranked search over encrypted cloud data has attracted considerable attention in recent studies, as it allows users to securely and efficiently retrieve highly relevant results. Traditional methods improve search efficiency by incorporating the K-means clustering algorithm. However, when applied to large-scale datasets, K-means can become computationally expensive. This paper introduces a multi-keyword ranked search method, SAO-KRS, which leverages the snow ablation optimizer (SAO) to enhance clustering performance. The approach begins with principal component analysis (PCA) to reduce the dimensionality of high-dimensional data, followed by clustering the reduced data using SAO, which reduces clustering overhead massively. By incorporating a heuristic best-first search algorithm over index trees, the scheme achieves reduced computational cost with high retrieval accuracy. In the best-case scenario, the proposed method achieves up to 21 times faster clustering and 2.7 times faster searching compared to the traditional K-means approach. Extensive experimental results verify that this method significantly improves clustering efficiency while ensuring both search speed and accuracy.