Publication

@inproceedings{鈴木孝男2017大気汚染センシングの実測データを用いた,

title = {大気汚染センシングの実測データを用いたNegative Surveysの復元精度推定手法の評価},

author = {鈴木孝男 and 伊藤昌毅 and 瀬崎薫},

url = {https://www.ieice.org/ken/paper/20170302qbRj/},

year  = {2017},

date = {2017-02-23},

booktitle = {ライフインテリジェンスとオフィス情報システム（LOIS）研究会},

journal = {電子情報通信学会技術研究報告= IEICE technical report: 信学技報},

volume = {116},

number = {488},

pages = {85--90},

publisher = {電子情報通信学会},

abstract = {環境センサを搭載したモバイルデバイスを持つ一般ユーザが環境センシングに参加するモバイル環境センシングにおいて，プライバシ情報の流出が問題となる．Perturbation技術を用いたプライバシ保護手法では，ユーザが手元のデバイスで偽装処理を施したセンサデータを送信することで個々のデータを秘匿化しつつ，サーバは特定の処理を施すことで偽装処理前の真のセンサデータの統計情報を復元できる．サーバはどの程度復元できているかを把握するために復元精度を推定する必要があり，筆者らは環境情報が時空間的相関に起因して特定のモデルに従うことを利用した復元精度推定手法を提案してきた．本稿では環境情報として大気汚染を取り上げ，筆者らが開発した大気汚染センシングデバイスを用いて行った大規模なモバイル大気汚染センシングについて報告し，得られた実測データを用いて，Perturbation技術の1つであるNegative Surveysの復元精度推定手法を評価する． 

In mobile environmental sensing, where mobile users sense environmental variables and send data to a server via mobile devices, there is a risk of privacy leakage from sensor readings. Data perturbation, a privacy preserving method, disguises sensor readings on the user side while allowing a server to reconstruct statistical information, such as data distribution, of original data. Data perturbation has a serious problem in that a server cannot calculate the reconstruction accuracy. We previously proposed a method to estimate the reconstruction accuracy using a spatiotemporal model of environmental variables. In this study, we collect air pollution data with our mobile air pollution sensing device. Using the field survey data, we evaluate our methods to estimate the reconstruction accuracy of Negative Surveys, a data perturbation technique.},

keywords = {Data Perturbation, Mobile Environmental Sensing, Negative Surveys, Privacy, Reconstruction Accuracy},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{6483674,

title = {Limited Negative Surveys: Privacy-preserving participatory sensing},

author = {Shunsuke Aoki and Masayuki Iwai and Kaoru Sezaki},

doi = {10.1109/CloudNet.2012.6483674},

isbn = {978-1-4673-2797-8},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET), 28-30 Nov. 2012, Paris, France},

journal = {2012 1st IEEE International Conference on Cloud Networking, CLOUDNET 2012 - Proceedings},

pages = {158-160},

abstract = {Participatory sensing is a crowd-sourcing technique that relies on participants' active contribution. By aggregating data sets from each participant, we can collect statistical information of the environment or phenomenona in the cloud. To promote use of participatory sensing in healthcare, research, and other useful applications, the protection of privacy is important to consider. The invasion of privacy in participatory sensing would have dire consequences because mobile phone used by these applications have sensitive data about users daily life. In this paper, we suggest a privacy-preserving participatory sensing scheme for real-world data sets with large numbers of categories by using Limited Negative Surveys. By using our method described in this paper, the server can reconstruct the probability distributions of the original distributions of sensed values without knowing the personal information of citizens. Furthermore, our research has the capablity to change, according to the features of data, especially the number of categories. We evaluate how this scheme of aggregate information can be still useful while protecting the privacy of users' original data.},

keywords = {Base stations, Data privacy, Educational institutions, Mobile handsets, Privacy, Protocols, sensors},

pubstate = {published},

tppubtype = {inproceedings}

}