Sangeetha S, Sudha Sadasivam G, Latha R
Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India.
Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India.
Big Data. 2021 Jun;9(3):203-218. doi: 10.1089/big.2020.0038. Epub 2021 Mar 18.
The Recommendation system relies on feedback and personal information collected from users for effective recommendation. The success of a recommendation system is highly dependent on storing and managing sensitive customer information. Users refrain from using the application if there is a threat to user privacy. Several works that were performed to protect user privacy have paid little attention to utility. Hence, there is a need for a robust recommendation system with high accuracy and privacy. Model-based approaches are more prevalent and commonly used in recommendation. The proposed work improvises the existing private model-based collaborative filtering algorithm with high privacy and utility. We identified that data sparsity is the primary reason for most of the threats in a recommender framework through an extensive literature survey. Hence, our approach combines the injection for imputing the missing ratings, which are deemed low, with differential privacy. We additionally introduce a random differential privacy approach to alternating least square (ALS) for improved utility. Experimental results on benchmarked datasets confirm that the performance of our private noisy Random ALS algorithm outperforms the non-noisy ALS for all datasets.
推荐系统依靠从用户收集的反馈和个人信息来进行有效推荐。推荐系统的成功高度依赖于对敏感客户信息的存储和管理。如果用户隐私受到威胁，用户会避免使用该应用程序。为保护用户隐私所做的一些工作很少关注实用性。因此，需要一个具有高精度和隐私性的强大推荐系统。基于模型的方法在推荐中更为普遍且常用。所提出的工作改进了现有的基于私有模型的协同过滤算法，具有高隐私性和实用性。通过广泛的文献调查，我们发现数据稀疏性是推荐框架中大多数威胁的主要原因。因此，我们的方法将用于估算被视为低评分的缺失评分的注入与差分隐私相结合。我们还引入了一种随机差分隐私方法用于交替最小二乘法（ALS）以提高实用性。在基准数据集上的实验结果证实，我们的私有噪声随机ALS算法在所有数据集上的性能均优于无噪声的ALS。
