We present a privacy preserving method based on inserting dummy data into original data on the data structure called Zero-suppressed BDDs (ZDDs). Our task is distributed itemset mining, which is frequent itemset mining from horizontally partitioned databases stored in distributed places called sites. We focus on the fundamental case in which there are two sites and each site has a database managed by its owner. By dividing the process of distributed itemset mining into the set union and the set intersection, we show how to make the operations secure in the sense of undistinguishability of data, which is our criterion for privacy preserving based on the already proposed criterion, p-indistinguishability. Our method conceals the original data in each operation by inserting dummy data, where ZDDs, BDD-based directed acyclic graphs, are adopted to represent sets of itemsets compactly and to implement the set operations in constructing the distributed itemset mining process. As far as we know, this is the first technique which gives a concrete representation of sets of itemsets and an implementation of set operations for privacy preserving in distributed itemset mining. Our experiments show that the proposed method provides undistinguishability of dummy data. Furthermore, we compare our method with Secure Multiparty Computation (SMC), which is one of the well-known techniques of secure computation.

Owing to the COVID-19 pandemic, many academic conferences are now being held online. Our study focuses on online video conferences, where participants can watch pre-recorded embedded videos on a conference website. In online video conferences, participants must efficiently find videos that match their interests among many candidates. There are few opportunities to encounter videos that they may not have planned to watch but may be of interest to them unless participants actively visit the conference. To alleviate these problems, the introduction of a recommender system seems promising. In this paper, we implemented typical recommender systems for the online video conference with 4,000 participants and analyzed users’ behavior through A/B testing. Our results showed that users receiving recommendations based on collaborative filtering had a higher continuous video-viewing rate and spent longer on the website than those without recommendations. In addition, these users were exposed to broader videos and tended to view more from categories that are usually less likely to view together. Furthermore, the impact of the recommender system was most significant among users who spent less time on the site.