The adoption of symmetric searchable encryption (SSE) has become increasingly common. However, many current SSE schemes assume an honest-but-curious cloud service provider (CSP) or necessitate significant overhead to manage a malicious CSP. Furthermore, most of these schemes are tailored for static datasets. Our paper presents an efficient SSE scheme that aims to address these challenges. To the best of our knowledge, this is the first scheme that supports dynamic datasets with forward and backward privacy, integrity verification of non-empty and empty search results, efficient search, non-interactive, light client, and both forward and inverted indexes simultaneously. In this paper, we present two novel approaches, Hexie and Jianding. Hexie implements secret sharing to conceal index entries, enabling dynamic updates, non-interactive interactions, and lightweight clients. To enhance the reliability of search results and address the problem of empty, incomplete, or inaccurate outcomes, we introduce the Jianding scheme as an extension of Hexie. It combines a chained MAC structure with a secret sharing scheme, which enables a client to verify the data integrity of the search result efficiently. Moreover, we propose graph-based dictionary sharding to enhance search efficiency. Finally, we conduct comprehensive experiments to validate the effectiveness of the proposed schemes.

Dynamic Searchable Symmetric Encryption (DSSE) allows cloud users to securely retrieve and update their data while outsourcing it to untrusted cloud service providers. Although extensive research efforts in recent years have notably improved the retrieval efficiency of DSSE, there remains potential for enhancing update efficiency, particularly in large-scale dataset updating scenarios. Therefore, we proposed a pioneering scheme called GPSSE (GPU-Accelerated Dynamic Searchable Symmetric Encryption Scheme) to explore accelerating batch updates of DSSE through GPU. In our design, we break the traditional chain-based data structure and build independent label-based data blocks to store entries. It facilitates the parallel updating of entries. Moreover, GPSSE achieves forward privacy and Type II backward privacy. In addition, we formally prove the security of the proposed GPSSE and show its practicality by conducting experiments using the publicly well-known Enron Email dataset. Experimental results demonstrate that GPSSE outperforms 40× to 194156× than other state-of-the-art schemes in updating efficiency.

Zero-shot object detection has shown its ability to overcome the problems of data scarcity and novel classes. Existing methods generally utilize static semantic vectors to classify objects and guide the network to map visual features to semantic vectors. However, the distribution of semantic vectors cannot adequately represent visual features, which makes migration from seen to unseen classes difficult. This work explores the dynamic semantic vector method to align the distributions of semantic vectors and visual features. The main challenge is to get a more reasonable distribution of semantic vectors. To address this issue, we proposed a two-way classification branch network and introduce N-pair loss into the dynamic semantic vector optimization process. Experiments on the MS-COCO dataset and SiTi (a real-world autonomous driving dataset collected by us) demonstrate the effectiveness and generalization of our method. Our code is available at https://github.com/HaoyuLizju/ZSD_tcb

Few-shot object detection is a challenging task because of the limited annotation data. Under the limitation of few-shot samples, images from the same class may differ significantly in appearance and pose. Although the research has progressed considerably since adding the prototype vector to few-shot object detection, the previous paradigm is still constrained by several factors: (1) using a single prototype to represent the support image tends to cause semantic ambiguity; (2) the way of extracting prototypes is too simple, like global average pooling, which makes prototypes not representative enough. In this work, we design PMR-CNN to address the above limitations. PMR-CNN proposes a new method of prototype generation and enhances the representative information by using multiple prototypes to represent support images. For experiments, we not only evaluate our method on general image dataset MS COCO, but also evaluate on SiTi (a real-world autonomous driving dataset collected by us). Experiment on the few-shot object detection benchmark shows that we have a significant advantage over the previous methods. Code is available at: https://github.com/Chientsung-Chou/PMR-CNN.