111 citations · 400 across the 24 of their papers we have counts for
25 papers
BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal Information
Jiexin Wang, Adam Jatowt, Masatoshi Yoshikawa +1
Time is an important aspect of documents and is used in a range of NLP and IR tasks. In this work, we investigate methods for incorporating temporal information during pre-training…
ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections
Jiexin Wang, Adam Jatowt, Masatoshi Yoshikawa
In the last few years, open-domain question answering (ODQA) has advanced rapidly due to the development of deep learning techniques and the availability of large-scale QA datasets…
Asymmetric Differential Privacy
Shun Takagi, Yang Cao, Masatoshi Yoshikawa
Differential privacy (DP) is getting attention as a privacy definition when publishing statistics of a dataset. This paper focuses on the limitation that DP inevitably causes two-s…
Quantifying the Privacy-Utility Trade-offs in COVID-19 Contact Tracing Apps
Patrick Ocheja, Yang Cao, Shiyao Ding +1
How to contain the spread of the COVID-19 virus is a major concern for most countries. As the situation continues to change, various countries are making efforts to reopen their ec…
Secure and Efficient Trajectory-Based Contact Tracing using Trusted Hardware
Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
The COVID-19 pandemic has prompted technological measures to control the spread of the disease. Private contact tracing (PCT) is one of the promising techniques for the purpose. Ho…
Geo-Graph-Indistinguishability: Location Privacy on Road Networks Based on Differential Privacy
Shun Takagi, Yang Cao, Yasuhito Asano +1
In recent years, concerns about location privacy are increasing with the spread of location-based services (LBSs). Many methods to protect location privacy have been proposed in th…
FLAME: Differentially Private Federated Learning in the Shuffle Model
Ruixuan Liu, Yang Cao, Hong Chen +2
Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differenti…
PANDA: Policy-aware Location Privacy for Epidemic Surveillance
Yang Cao, Shun Takagi, Yonghui Xiao +2
In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from coronavirus crises attempt to access citizen's locati…
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model
Shun Takagi, Tsubasa Takahashi, Yang Cao +1
How can we release a massive volume of sensitive data while mitigating privacy risks? Privacy-preserving data synthesis enables the data holder to outsource analytical tasks to an…
PGLP: Customizable and Rigorous Location Privacy through Policy Graph
Yang Cao, Yonghui Xiao, Shun Takagi +6
Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off b…
Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release
Yaowei Han, Sheng Li, Yang Cao +2
With the development of smart devices, such as the Amazon Echo and Apple's HomePod, speech data have become a new dimension of big data. However, privacy and security concerns may…
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection
Ruixuan Liu, Yang Cao, Masatoshi Yoshikawa +1
As massive data are produced from small gadgets, federated learning on mobile devices has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) has…
Toward a view-based data cleaning architecture
Toshiyuki Shimizu, Hiroki Omori, Masatoshi Yoshikawa
Big data analysis has become an active area of study with the growth of machine learning techniques. To properly analyze data, it is important to maintain high-quality data. Thus,…
Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services
Yang Cao, Yonghui Xiao, Li Xiong +2
Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users' location privacy by releasing a perturbed location to third parties such as locat…
Trading Location Data with Bounded Personalized Privacy Loss
Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa
As personal data have been the new oil of the digital era, there is a growing trend perceiving personal data as a commodity. Although some people are willing to trade their persona…
Blockchain-based Bidirectional Updates on Fine-grained Medical Data
Chunmiao Li, Yang Cao, Zhenjiang Hu +1
Electronic medical data sharing between stakeholders, such as patients, doctors, and researchers, can promote more effective medical treatment collaboratively. These sensitive and…
When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy
Maho Asada, Masatoshi Yoshikawa, Yang Cao
In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and lea…
Making View Update Strategies Programmable - Toward Controlling and Sharing Distributed Data -
Yasuhito Asano, Soichiro Hidaka, Zhenjiang Hu +10
Views are known mechanisms for controlling access of data and for sharing data of different schemas. Despite long and intensive research on views in both the database community and…
Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training
Bei Liu, Jianlong Fu, Makoto P. Kato +1
Automatic generation of natural language from images has attracted extensive attention. In this paper, we take one step further to investigate generation of poetic language (with m…
A View-based Programmable Architecture for Controlling and Integrating Decentralized Data
Yasuhito Asano, Soichiro Hidaka, Zhenjiang Hu +9
The view and the view update are known mechanism for controlling access of data and for integrating data of different schemas. Despite intensive and long research on them in both t…
Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations
Yang Cao, Masatoshi Yoshikawa, Yonghui Xiao +1
Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as…
How to Balance Privacy and Money through Pricing Mechanism in Personal Data Market
Rachana Nget, Yang Cao, Masatoshi Yoshikawa
A personal data market is a platform including three participants: data owners (individuals), data buyers and market maker. Data owners who provide personal data are compensated ac…
Quantifying Differential Privacy under Temporal Correlations
Yang Cao, Masatoshi Yoshikawa, Yonghui Xiao +1
Differential Privacy (DP) has received increased attention as a rigorous privacy framework. Existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primi…
Your Neighbors Are My Spies: Location and other Privacy Concerns in GLBT-focused Location-based Dating Applications
Nguyen Phong Hoang, Yasuhito Asano, Masatoshi Yoshikawa
Trilateration is one of the well-known threat models to the user's location privacy in location-based apps, especially those contain highly sensitive information such as dating app…