Searchable symmetric encryption (SSE) in the multi-user setting is designed for scenarios where data owners outsource their encrypted data to the cloud while allowing legitimate data users to search on it. However, existing multi-user SSE schemes are not practical in real scenarios with hierarchical user structure such as enterprises and hospitals. Specifically, most schemes require real-time participation of data owners in the authorization or search process, and are not efficient in authorization adjustment, placing a large computational burden on them. In this paper, we focus on hierarchical authorization in the multi-user setting and propose a forward secure scheme, called DSSEHA. In particular, we develop a hierarchical authorization mechanism where the data owner chooses to share her/his data with specific legitimate users who can continue to share with low-level users, thus reducing computational pressure on the data owner. Experiments show that the computation cost of DSSEHA in search is close to the state-of-the-art solution, while the computation cost in update and authorization (e.g., less than 0.1 ms per document for online authorization and less than 0.6 ms for offline authorization) and storage cost (e.g., less than 50.7 MB for Enron subset with 10,000 documents) are much smaller than existing schemes.

Similarity search on encrypted data can identify similar data and handle misspelled keywords in a privacy-preserving manner and thus has received a lot of attention. However, existing schemes suffer from imprecise or predefined distance thresholds, which means that they do not always return the expected search results. Moreover, these schemes either do not consider document addition or lack forward security in this dynamic setting. In this article, we present a Similar Keyword Matching (SKM) framework that accurately calculates the Hamming distance between keywords through a new keyword representation called uni-pos-gram. Based on our framework, we propose a basic scheme for similarity search over encrypted data called SimSE that offers adjustable Hamming distance thresholds and an enhanced scheme called SimSE-F that provides forward security. Security analysis demonstrates that our schemes effectively safeguard the privacy of documents, indexes, and searches. Empirical experiments using real-world datasets demonstrate the efficiency and practical applicability of our schemes.

Dynamic searchable symmetric encryption (DSSE) enables updates and keyword searches on outsourced encrypted data while minimizing the information revealed to the server. However, existing DSSE schemes that support conjunctive keyword searches disclose added documents or fail to filter deleted ones in certain circumstances, thus violating forward and backward privacy. Besides, the size of their search tokens increases with the number of documents, which incurs a heavy communication cost. In this paper, we develop a conjunctive DSSE scheme that has a search token size only related to the conjunction size and fully supports forward and backward privacy. Our scheme is based on a new three-dimensional chain structure called CUBE. We also rethink the leakage function of conjunctive queries and prove that our scheme satisfies the refined security definition. Experimental results demonstrate that compared with the state-of-the-art schemes, our scheme increases the computational cost by at most 9.62% but reduces the communication cost by 99.78% when searching six conjunctive keywords.

Large Language Models (LLMs) such as GPT & Llama have demonstrated significant achievements in summarization tasks but struggle with factual inaccuracies, a critical issue in clinical NLP applications where errors could lead to serious consequences.To counter the high costs and limited availability of expert-annotated data for factual alignment, this study introduces an innovative pipeline that utilizes >100B parameter GPT variants like GPT-3.5 & GPT-4 to act as synthetic experts to generate high-quality synthetics feedback aimed at enhancing factual consistency in clinical note summarization.Our research primarily focuses on edit feedback generated by these synthetic feedback experts without additional human annotations, mirroring and optimizing the practical scenario in which medical professionals refine AI system outputs.Although such 100B+ parameter GPT variants have proven to demonstrate expertise in various clinical NLP tasks, such as the Medical Licensing Examination, there is scant research on their capacity to act as synthetic feedback experts and deliver expert-level edit feedback for improving the generation quality of weaker (<10B parameter) LLMs like GPT-2 (1.5B) & Llama 2 (7B) in clinical domain.So in this work, we leverage 100B+ GPT variants to act as synthetic feedback experts offering expert-level edit feedback, that is used to reduce hallucinations and align weaker (<10B parameter) LLMs with medical facts using two distinct alignment algorithms (DPO & SALT), endeavoring to narrow the divide between AIgenerated content and factual accuracy.This highlights the substantial potential of LLMbased synthetic edits in enhancing the alignment of clinical factuality 1 . * indicates equal contribution † Presently in AMD AI

The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each perspective, leading to view inconsistency. In this work, we first model and analyze the problem, visualizing the specific causes of the Janus Problem, which are associated with discrete view encoding and shared priors in 2D lifting. Based on this, we further propose the LCGen method, which guides text-to-3D to obtain different priors with different certainty from various viewpoints, aiding in view-consistent generation. Experiments have proven that our LCGen method can be directly applied to different SDS-based text-to-3D methods, alleviating the Janus Problem without introducing additional information, increasing excessive training burden, or compromising the generation effect.