Searchable symmetric encryption (SSE) enables the storage and retrieval of encrypted data on untrusted cloud servers, while dynamic searchable symmetric encryption (DSSE) further supports updating encrypted data. To date, in multi-user environments, most DSSE schemes cannot achieve simultaneous access control for both keyword retrieval and data updates. To address this issue, we propose a new DSSE scheme with efficient and complete(keyword retrieval and update)access control for multi-user environments, named EFCAM. Our work has simultaneously achieved efficient, flexible, and fine-grained access control for keyword retrieval and updating, this is extremely rare in existing research. For update operations, we combine file index encoding and homomorphic encryption (HE) technology, so that EFCAM optimizes the calculation; to achieve flexible access control, we adopt an equality test scheme that can supports three types of update authorization. For retrieval operations, users do not need to share keys. By executing a single query, the users can effectively retrieve all the data that they have permission to access. To enhance system security and operational efficiency, we have extended EFCAM with a dynamic policy update mechanism for flexible and real-time adjustment of access control policies. We formally analyze the security of EFCAM to prove that our scheme has forward security (FS) and backward security (BS). Experimental results show that, EFCAM maintains outstanding efficiency in encrypted data retrieval and update operations within multi-user environments, while also exhibiting strong scalability.

Fuzz testing is an dynamic program analysis technique designed for discovering vulnerabilities in IoT systems. The core goal is to deliberately feed maliciously crafted inputs into an IoT device or service, triggering vulnerabilities such as system crashes, buffer overflow exploits, and memory corruption, etc. Efficiently generating malicious inputs remains challenging, with leading methods often relying on randomly mutating existing valid inputs. In this work, we propose to adopt fine-tuned large language models (FuzzCoder) to learn patterns in the input files from successful attacks to guide future fuzzing explorations. Specifically, we develop a framework that leverages code LLMs to guide the mutation process to perform meaningful input mutations. We formulate the mutation process as the sequenceto-sequence modeling, where LLM receives a sequence of bytes and outputs the mutated byte sequence. FuzzCoder is fine-tuned on our created instruction dataset (FuzzInstruct), where the successful fuzzing history is collected from the heuristic fuzzing tool. FuzzCoder can predict mutation positions and strategies for input files to trigger abnormal behaviors of the program. Most importantly, the experiment reveals results that FuzzCoder achieves better fuzzing performance compared to traditional and other AFL-based fuzzers, such as AFL, AFL++, AFLSmart, etc. On average, FuzzCoder achieves an improvement in code coverage of more than 20%, along with a significant increase in the number of crashes. 1