Federated learning is a method where multiple participants collaboratively train a model using machine learning techniques, such as deep learning, while keeping each participant's data private through the use of a central server. However, there are cases in which participant input information is leaked to the server. To solve this problem, several protocols have been proposed. In particular, Phong et al. (in IEEE TIFS 2018) proposed a protocol that protects participant information against a server using homomorphic encryption. Later, Park et al. (in ICTC 2022) proposed an improved protocol, where each client has a different secret-key for the homomorphic encryption. In this paper, we show that the Park et al.'s protocol has some weakness and propose the secure one based on the previously proposed protocols.

For a better understanding of anonymization methods for location traces, we have designed and held a location trace anonymization contest that deals with a long trace (400 events per user) and fine-grained locations (1024 regions). In our contest, each team anonymizes her original traces, and then the other teams perform privacy attacks against the anonymized traces. In other words, both defense and attack compete together, which is close to what happens in real life. Prior to our contest, we show that re-identification alone is insufficient as a privacy risk and that trace inference should be added as an additional risk. Specifically, we show an example of anonymization that is perfectly secure against re-identification and is not secure against trace inference. Based on this, our contest evaluates both the re-identification risk and trace inference risk and analyzes their relationship. Through our contest, we show several findings in a situation where both defense and attack compete together. In particular, we show that an anonymization method secure against trace inference is also secure against re-identification under the presence of appropriate pseudonymization. We also report defense and attack algorithms that won first place, and analyze the utility of anonymized traces submitted by teams in various applications such as POI recommendation and geo-data analysis.

PRINCESS (Proxy Re-encryption with INd-Cca security in an Encrypted file Storage System) is a secure storage system which utilizes special proxy re-encryption technology. With PRINCESS, the files encrypted in accordance with the confidentiality levels can be shared among appointed users while remaining encrypted. In this poster/demo, we show the efficiency of PRINCESS, which can be applied to a Body Area Network information sharing, automobile information sharing, etc. This system facilitates the potential for new services that require privacy data to be shared securely via cloud technology.