We will have keynote talks from the following world-leading researchers.
Prof. Dario Fiore
IMDEA Software Institute, Spain
Vector commitments allow a party to commit to a vector and then to open the commitment at selected positions. The crucial feature of this primitive is that the size of both commitments and openings does not depend on the length of the vector. Thanks for this property, vector commitments can provide security and efficiency in applications where clients outsource the storage of large data to external parties and they want to check that the data has not been tampered with. In this talk I will present the notion of vector commitments, give an overview of the state of the art in this area, and cover some of the recent efficient constructions. I will also discuss applications and open problems.
Dr. Takao Murakami
A local privacy model has recently attracted much attention as a privacy model that does not require a trusted third party. In particular, LDP (Local Differential Privacy) is known as a gold standard for data privacy in this model. It is well known that statistical information can be accurately estimated under LDP when the number of users is very large and each user’s data is independently generated from an underlying distribution. However, accurate data analysis under LDP is very challenging for a small number of users or for more complex data.
In this talk, I will introduce some recent studies to address this issue. First, I will present a privacy metric in the local privacy model that enables accurate data analysis for a small number of users while providing LDP for sensitive data. Then, I will present algorithms for analysis of graph statistics under LDP as an example of data analysis for complex data.