In recent years, rapid digitization and technological advances have enabled governments and companies to utilize various types of personal information, including environment, medical, and lifestyle. Privacy protection is an important issue in the utilization of personal information, and anonymization methods such as k-anonymization and 1-diversity have been proposed in the past. However, to utilize data effectively, it is also important not to lose the usefulness of the information. Privacy protection and preserving the usefulness of information are in a trade-off relationship, and balancing these two aspects has been a difficult problem. Recently, microaggregation using clustering algorithms has been attracting attention as an anonymization method that ensures the usefulness of the data while protecting privacy. In this study, we proposed a new microaggregation method based on iterative processing using probabilistic latent semantic analysis and evaluated the proposed method based on the use case. The use case is a classification of social isolation and an analysis of the relationship with physical frailty risk using the classified groups. Compared to microaggregation using quasi-identifiers and microaggregation based on a conventional clustering algorithm, the proposed method was able to find social isolation characteristics obtained in the raw data with higher accuracy. In addition, in a more detailed analysis focusing on a population with certain characteristics, the proposed method was able to replicate the analysis results obtained using the raw data and to make appropriate assessments of the health risks.

In this study, we constructed a model for predicting the future frailty status of individuals based on information regarding their current lifestyle habits. In recent years, as the global population ages, the number of people certified for long-term care has increased. In an effort to prevent the need for long-term care, prediction of the onset of frailty, which is positioned as a preliminary stage of the caregiving state, is gaining attention. However, the onset of frailty takes a long time, and the incidence of frailty differs according to one’s health literacy. Therefore, a prediction is extremely difficult to achieve. In this study, we constructed a prediction model using a probabilistic latent semantic analysis and a Bayesian network. First, the probabilistic latent semantic analysis was conducted to classify individuals according to their health literacy. Second, we constructed a future frailty prediction model using a Bayesian network for each health literacy segment. As a result, we clarified the following: It is more appropriate to predict a future frailty by predicting the future lifestyle using information on the current lifestyle. In addition, more accurate prediction models can be constructed when individuals are divided based on their health literacy.