In recent technological advancement, the health recommendation system is gaining attention among the public to acquire health care services online. Traditional health recommendations are insecure due to the lack of security constraints caused by the intruders and not suitable to suggest appropriate recommendations. Thus, it creates hesitation in the minds of the people to share sensitive medical information. Hence, it is essential to design a privacy-preserving health recommendation system that should guarantee privacy and also suggest top-N recommendation to the user based on their preferences and earlier feedback. To cope with these issues, we propose a stacked discriminative de-noising convolution auto-encoder–decoder with a two-way recommendation scheme that provides secure and efficient health data to the end-users. In this scheme, privacy is assured to users through the modified blowfish algorithm. For structuring the big data collected from the patient, the Hadoop transform is used. Here, the two-way system analyzes and learns more effective features from the explicit and implicit information of the patient individually, and finally, all the learned features are fused to provide an efficient recommendation. The performance of the proposed system is analyzed with different statistical metrics and compared with recent approaches. From the result analysis, it is evident that the proposed system performs better than the earlier approaches.

随着最近的技术进步，健康推荐系统越来越受到公众的关注，以在线获取健康护理服务。由于缺乏入侵者造成的安全约束，传统的健康建议不安全，因此不适合提出适当的建议。因此，它使人们在共享敏感的医学信息时犹豫不决。因此，至关重要的是设计一种隐私保护的健康推荐系统，该系统应确保隐私并根据用户的偏好和较早的反馈向用户建议top-N推荐。为了解决这些问题，我们提出了一种具有区分性的堆叠式降噪卷积自动编码器-解码器，该编码器具有双向推荐方案，可以为最终用户提供安全有效的健康数据。在这个方案中 通过改进的河豚算法可以确保用户的隐私。为了构建从患者那里收集的大数据，使用了Hadoop转换。在此，双向系统分别从患者的显式和隐式信息中分析和学习更有效的功能，最后，将所有学习到的功能融合在一起，以提供有效的建议。用不同的统计指标分析了所提出系统的性能，并与最新方法进行了比较。从结果分析可以明显看出，所提出的系统比以前的方法具有更好的性能。双向系统分别从患者的显式和隐式信息中分析和学习更有效的功能，最后，将所有学习到的功能融合在一起，以提供有效的建议。用不同的统计指标分析了所提出系统的性能，并与最新方法进行了比较。从结果分析可以明显看出，所提出的系统比以前的方法具有更好的性能。双向系统分别从患者的显式和隐式信息中分析和学习更有效的功能，最后，将所有学习到的功能融合在一起以提供有效的建议。用不同的统计指标分析了所提出系统的性能，并与最新方法进行了比较。从结果分析可以明显看出，所提出的系统比以前的方法具有更好的性能。