Advanced Research on Data Privacy in the ARES Project

Navarro‐Arribas, Guillermo; Torra, Vicenç

doi:10.1007/978-3-319-09885-2_1

Cited by 7 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Privacy Issues: Machine learning and data mining communities as well as other fields including policy, security and governance have been working on these issues for some time. From an analytics perspective, privacy preserving data mining has developed into a subfield and considerable effort has gone into studying privacy challenges in data mining (Matwin 2013, Navarro-Arribas andTorra 2014), data publishing (Fung et al 2010) and, to some extent, integration and interactions of sensors (Aggarwal and Abdelzaher 2011). However, these efforts have focused mainly on the data and analytics layers.…”

Section: Societal Impact and Areas Of Concernsmentioning

confidence: 99%

Big Data and the Internet of Things

Shah¹

2015

Studies in Big Data

View full text Add to dashboard Cite

Abstract. Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things -IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.

show abstract

Section: Societal Impact and Areas Of Concernsmentioning

confidence: 99%

Big Data and the Internet of Things

Shah¹

2015

Studies in Big Data

View full text Add to dashboard Cite

show abstract

“…Privacy in the psychological sense is widely agreed to refer to an individual's ability to regulate how much information about the self is known to others (e.g., Westin, 1967). Security in a technological sense refers to methods for preventing disclosure of sensitive information to unintended recipients, a field sometimes referred to as privacy enhancing technologies (PET: Navarro-Arribas & Torra, 2015). Compliance in a legal sense refers to whether an organization's policies and procedures conform to the requirements of relevant legislation (e.g., Herrmann, 2007).…”

Section: Data Privacy and Informed Consent Still Matter In The Era Ofmentioning

confidence: 99%

I-Os in the Vanguard of Big Data Analytics and Privacy

Ducey¹,

Guenole²,

Weiner³

et al. 2015

Ind. Organ. Psychol.

View full text Add to dashboard Cite

In this response to Guzzo, Fink, King, Tonidandel, and Landis (2015), we suggest industrial–organizational (I-O) psychologists join business analysts, data scientists, statisticians, mathematicians, and economists in creating the vanguard of expertise as we acclimate to the reality of analytics in the world of big data. We enthusiastically accept their invitation to share our perspective that extends the discussion in three key areas of the focal article—that is, big data sources, logistic and analytic challenges, and data privacy and informed consent on a global scale. In the subsequent sections, we share our thoughts on these critical elements for advancing I-O psychology's role in leveraging and adding value from big data.

show abstract

“…Data integration across autonomous data sources has been a long-standing challenge for the database community [6], [5], [4]. This is motivated by the number of contexts in which the need for a flexible data integration mechanism is critical, including Web and enterprise data integration, integrating data pieces from smart objects in IoT-based smart environments, data sharing for scientific research, data exchange in government agencies, etc.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Aware Cloud Services Composition

Ghafour

Ghodous

Bonnet

2015

2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises

View full text Add to dashboard Cite

We propose a service-oriented privacy-preserving model for data integration across autonomous clouds. Our model allows to execute aggregations of cloud data sharing services without revealing any extra information to any of the involved services, i.e., none of involved services nor their providers are able to learn/infer any information about the data the other services provide beyond what these services already know.

show abstract

Advanced Research on Data Privacy in the ARES Project

Cited by 7 publications

References 34 publications

Big Data and the Internet of Things

Big Data and the Internet of Things

I-Os in the Vanguard of Big Data Analytics and Privacy

Privacy-Aware Cloud Services Composition

Contact Info

Product

Resources

About