Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

プロファイルと履歴を組織間で統合利用するプライバシ保護推薦システム

電気通信大学201

Maintaining privacy during continuous motion sensing

Mobile devices contain sensors which allow continuous recording of a user's motion allowing the development of activity, fitness and health applications. With varied applications, the motion sensors present new privacy problems which require protection. This dissertation builds on previous work with activity and fitness machine learning techniques demonstrating the ability to predict medical values from motion data using smartphones. We conduct two clinical trials collecting a data set of eighty-eight patients and forty-five hours of monitoring to analyze the privacy implications of releasing motion data. We extract a comprehensive set of statistical features from all available smartphone sensors and evaluate feature selection techniques and machine learning models. We find we can predict user identity, phone identity, speed, FEV1/FVC, and activity from the motion signal. Designing a privacy protection mechanism for motion data requires a precise understanding of how the signal predicts the sensitive information. We develop algorithms to conduct private feature selection which identifies features useful for prediction. We find that simply blocking all private features significantly reduces the usefulness of the signal for other predictions. We develop a sensitivity estimation framework to calibrate the noise for each private feature requiring an order of magnitude less noise than differential privacy sensitivity. We find adding noise to private features calibrated using the sensitivity estimate is effective at reducing the prediction of five tested target predictions. Our methods hide both user and phone identification while allowing other prediction but cannot hide activity, FEV1/FVC and speed without significantly lowering the accuracy of other predictions. Our methods are still effective when the attacker has prior knowledge of the noise distribution. The methods presented in this dissertation demonstrate the need for privacy in motion data and provide a framework for protecting sensitive user information in motion readings

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute

Social informatics

5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p