Luo Qingcai, Li Hui
School of Cyber Engineering, Xidian University, Xi'an 710126, China.
School of Computer Science and Technology, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2024 Feb 4;24(3):1015. doi: 10.3390/s24031015.
Since the popularity of Machine Learning as a Service (MLaaS) has been increasing significantly, users are facing the risk of exposing sensitive information that is not task-related. The reason is that the data uploaded by users may include some information that is not useful for inference but can lead to privacy leakage. One straightforward approach to mitigate this issue is to filter out task-independent information to protect user privacy. However, this method is feasible for structured data with naturally independent entries, but it is challenging for unstructured data. Therefore, we propose a novel framework, which employs a spectrum-based encoder to transform unstructured data into the latent space and a task-specific model to identify the essential information for the target task. Our system has been comprehensively evaluated on three benchmark visual datasets and compared to previous works. The results demonstrate that our framework offers superior protection for task-independent information and maintains the usefulness of task-related information.
由于机器学习即服务（MLaaS）的普及率显著提高，用户面临着暴露与任务无关的敏感信息的风险。原因在于用户上传的数据可能包含一些对推理无用但会导致隐私泄露的信息。缓解此问题的一种直接方法是过滤掉与任务无关的信息以保护用户隐私。然而，这种方法对于具有自然独立条目的结构化数据是可行的，但对于非结构化数据则具有挑战性。因此，我们提出了一种新颖的框架，该框架采用基于频谱的编码器将非结构化数据转换到潜在空间，并使用特定任务模型来识别目标任务的关键信息。我们的系统已在三个基准视觉数据集上进行了全面评估，并与先前的工作进行了比较。结果表明，我们的框架为与任务无关的信息提供了卓越的保护，并保持了与任务相关信息的有用性。
