Privacy-Aware Web APIs Recommendation for Consumer Mashup Creation Based on Iterative Quantification - Zhang, Ruowei; Qi, Lianyong; Yan, Chao; Chen, Zige; Gong, Wenwen; Xu, Yanwei; Rafique, Wajid (IEEE transactions on consumer electronics, 2025)
With the emergence of the "unmanned" field, unmanned supermarket software has entered consumers' lives in line with the pace of development of the times. Nowadays, developers of unmanned supermarket software often tend to create feature-rich consumer mashup applications by invoking a variety of Web APIs (Application Programming Interfaces) in order to save time and costs. The number of APIs available on edge servers has significantly increased with the rise of mobile edge computing. To cope with the continuous growth in API volume and the increasing diversity of API functions in API sharing communities (e.g., ProgrammableWeb.com), lightweight recommendation techniques have been employed to assist consumer mashup developers in finding their desired Web APIs from a vast pool of candidates. However, during the data processing in edge computing, traditional Web API recommendation approaches often prioritize the accuracy of recommended APIs, neglecting the potential privacy risks associated with disclosure. This significantly reduces the willingness and incentive of consumer mashup developers to share API information, such as historical edge mashup-API invocation records. To address this issue, we propose a privacy-preserving Web API recommendation approach, WARecITQ, based on Iterative Quantification (ITQ). Specifically, we convert the sensitive mashup-API invocation records into less-sensitive mashup indices using Iterative Quantization hash coding. We then utilize these less-sensitive mashup indices as the primary decision-making criteria for Web API recommendations, thereby achieving the goal of completing top-k API recommendations. Lastly, we crawled a real-world dataset of mashup-API invocation records from ProgrammableWeb.com and conducted a series of experiments on the dataset to evaluate performance. The results of the experiments demonstrate the superiority of our WARecITQ approach in multiple performance metrics compared to other related approaches, while ensuring consumer mashup developer privacy.