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Added by
Arkadiusz Stopczynski Mr.
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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Human brain activity collected in the form of Electroencephalography (EEG), even with low number of sensors, is an extremely rich signal. Traces collected from multiple channels and with high sampling rates capture many important aspects of participants brain activity and can be used as a unique personal identifier. The motivation for sharing EEG signals is significant, as a mean to understand the relation between brain activity and well-being, or for communication with medical services. As the equipment for such data collection becomes more available and widely used, the opportunities for using the data are growing; at the same time however inherent privacy risks are mounting. The same raw EEG signal can be used for example to diagnose mental diseases, find traces of epilepsy, and decode personality traits. The current practice of the informed consent of the participants for the use of the data either prevents reuse of the raw signal or does not truly respect participants right to privacy by reusing the same raw data for purposes much different than originally consented to. Here we propose an integration of a personal neuroinformatics system, Smartphone Brain Scanner, with a general privacy framework openPDS. We show how raw high-dimensionality data can be collected on a mobile device, uploaded to a server, and subsequently operated on and accessed by applications or researchers, without disclosing the raw signal. Those extracted features of the raw signal, called answers, are of significantly lower-dimensionality, and provide the full utility of the data in given context, without the risk of disclosing sensitive raw signal. Such architecture significantly mitigates a very serious privacy risk related to raw EEG recordings floating around and being used and reused for various purposes.
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There has been vigorous debate on how different countries responded to the COVID-19 pandemic. To secure public safety, South Korea actively used personal information at the risk of personal privacy whereas France encouraged voluntary cooperation at the risk of public safety. In this article, after a brief comparison of contextual differences with France, we focus on South Koreas approaches to epidemiological investigations. To evaluate the issues pertaining to personal privacy and public health, we examine the usage patterns of original data, de-identification data, and encrypted data. Our specific proposal discusses the COVID index, which considers collective infection, outbreak intensity, availability of medical infrastructure, and the death rate. Finally, we summarize the findings and lessons for future research and the policy implications.
The introduction of robots into our society will also introduce new concerns about personal privacy. In order to study these concerns, we must do human-subject experiments that involve measuring privacy-relevant constructs. This paper presents a taxonomy of privacy constructs based on a review of the privacy literature. Future work in operationalizing privacy constructs for HRI studies is also discussed.
Government statistical agencies collect enormously valuable data on the nations population and business activities. Wide access to these data enables evidence-based policy making, supports new research that improves society, facilitates training for students in data science, and provides resources for the public to better understand and participate in their society. These data also affect the private sector. For example, the Employment Situation in the United States, published by the Bureau of Labor Statistics, moves markets. Nonetheless, government agencies are under increasing pressure to limit access to data because of a growing understanding of the threats to data privacy and confidentiality. De-identification - stripping obvious identifiers like names, addresses, and identification numbers - has been found inadequate in the face of modern computational and informational resources. Unfortunately, the problem extends even to the release of aggregate data statistics. This counter-intuitive phenomenon has come to be known as the Fundamental Law of Information Recovery. It says that overly accurate estimates of too many statistics can completely destroy privacy. One may think of this as death by a thousand cuts. Every statistic computed from a data set leaks a small amount of information about each member of the data set - a tiny cut. This is true even if the exact value of the statistic is distorted a bit in order to preserve privacy. But while each statistical release is an almost harmless little cut in terms of privacy risk for any individual, the cumulative effect can be to completely compromise the privacy of some individuals.
The question we raise through this paper is: Is it economically feasible to trade consumer personal information with their formal consent (permission) and in return provide them incentives (monetary or otherwise)?. In view of (a) the behavioral assumption that humans are `compromising beings and have privacy preferences, (b) privacy as a good not having strict boundaries, and (c) the practical inevitability of inappropriate data leakage by data holders downstream in the data-release supply-chain, we propose a design of regulated efficient/bounded inefficient economic mechanisms for oligopoly data trading markets using a novel preference function bidding approach on a simplified sellers-broker market. Our methodology preserves the heterogeneous privacy preservation constraints (at a grouped consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfies information demand (via the broker) of agencies (e.g., advertising organizations) that collect client data for the purpose of targeted behavioral advertising.
News recommendation and personalization is not a solved problem. People are growing concerned of their data being collected in excess in the name of personalization and the usage of it for purposes other than the ones they would think reasonable. Our experience in building personalization products for publishers while adhering to safeguard user privacy led us to investigate more on the user perspective of privacy and personalization. We conducted a survey to explore peoples experience with personalization and privacy and the viewpoints of different age groups. In this paper, we share our major findings with publishers and the community that can inform algorithmic design and implementation of the next generation of news recommender systems, which must put the human at its core and reach a balance between personalization experiences and privacy to reap the benefits of both.
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