The significance of individuals' location information has been increasing recently, and the utilization of such data has become indispensable for businesses and society. The possible uses of location information include personalized services (maps, restaurant searches and weather forecast services) and business decisions (deciding where to open a store). However, considering that the data could be exploited, users should add random noise using their terminals before providing location data to collectors. In numerous instances, the level of privacy protection a user requires depends on their location. Therefore, in our framework, we assume that users can specify different privacy protection requirements for each location utilizing the adversarial error (AE), and the system computes a mechanism to satisfy these requirements. To guarantee some utility for data analysis, the maximum error in outputting the location should also be output. In most privacy frameworks, the mechanism for adding random noise is public; however, in this problem setting, the privacy protection requirements and the mechanism must be confidential because this information includes sensitive information. We propose two mechanisms to address privacy personalization. The first mechanism is the individual exponential mechanism, which uses the exponential mechanism in the differential privacy framework. However, in the individual exponential mechanism, the maximum error for each output can be used to narrow down candidates of the actual location by observing outputs from the same location multiple times. The second mechanism improves on this deficiency and is called the donut mechanism, which uniformly outputs a random location near the location where the distance from the user's actual location is at the user-specified AE distance. Considering the potential attacks against the idea of donut mechanism that utilize the maximum error, we extended the mechanism to counter these attacks. We compare these two mechanisms by experiments using maps constructed from artificial and real world data.

In this study, we generate dialogue contents in which two systems discuss their distress with each other. The user inputs sentences that include environment and feelings of distress. The system generates the dialogue content from the input. In this study, we created dialogue data about distress in order to generate them using deep learning. The generative model fine-tunes the GPT of the pre-trained model using the TransferTransfo method. The contribution of this study is the creation of a conversational dataset using publicly available data. This study used EmpatheticDialogues, an existing empathetic dialogue dataset, and Reddit r/offmychest, a public data set of distress. The models fine-tuned with each data were evaluated both automatically (such as by the BLEU and ROUGE scores) and manually (such as by relevance and empathy) by human assessors.