Privacy-Preserving Distributed Movement Data Aggregation

Monreale, Anna; Wang, Wendy Hui; Pratesi, Francesca; Rinzivillo, Salvatore; Pedreschi, Dino; Andrienko, Gennady; Andrienko, Natalia

doi:10.1007/978-3-319-00615-4_13

Cited by 25 publications

(24 citation statements)

References 24 publications

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“…when gaining a better understanding of socio-economic problems, how they arise and how they can be addressed. Thus, the task is to find a privacy-respecting way of socio-economic data mining without making this endeavor impossible [28,29].…”

Section: Adressing the Issue: Privacy By Designmentioning

confidence: 99%

FuturICT — The road towards ethical ICT

Hoven

Helbing

Pedreschi

et al. 2012

Eur. Phys. J. Spec. Top.

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Abstract. The pervasive use of information and communication technology (ICT) in modern societies enables countless opportunities for individuals, institutions, businesses and scientists, but also raises difficult ethical and social problems. In particular, ICT helped to make societies more complex and thus harder to understand, which impedes social and political interventions to avoid harm and to increase the common good. To overcome this obstacle, the large-scale EU flagship proposal FuturICT intends to create a platform for accessing global human knowledge as a public good and instruments to increase our understanding of the information society by making use of ICT-based research. In this contribution, we outline the ethical justification for such an endeavor. We argue that the ethical issues raised by FuturICT research projects overlap substantially with many of the known ethical problems emerging from ICT use in general. By referring to the notion of Value Sensitive Design, we show for the example of privacy how this core value of responsible ICT can be protected in pursuing research in the framework of FuturICT. In addition, we discuss further ethical issues and outline the institutional design of FuturICT allowing to address them. The Moral Case for FuturICTSocieties in the 21st century have reached a level of complexity that has made our understanding of them deeply problematic. Moreover, whatever provisionary understanding we manage to achieve is often outpaced by rapid change and new developments, making social and political interventions difficult. A major driving force of change is information and communication technology (ICT) that created new instruments for social exchange (e.g., social media, social network sites). Political decision makers, however, often have difficulties in keeping track of new media developments and in coming to grips with their dynamics and impact upon society and their varia e-mail: M.J.vandenHoven@tudelft.nl

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Section: Adressing the Issue: Privacy By Designmentioning

confidence: 99%

FuturICT — The road towards ethical ICT

Hoven

Helbing

Pedreschi

et al. 2012

Eur. Phys. J. Spec. Top.

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“…Existing methods either act on the network layer (Kopp et al 2012) or inspired by the differential privacy paradigm they add random noise (Monreale et al 2013). The work in Clifton et al (2004) denotes a protocol for secure aggregation among multiple parties, but their algorithm requires extensive communication among the parties and is infeasible in the considered crowd sourcing (i.e.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Centralized Counting of Moving Objects

Liebig

2015

Lecture Notes in Geoinformation and Cartography

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Proliferation of pervasive devices capturing sensible data streams, e.g. mobility records, raise concerns on individual privacy. Even if the data is aggregated at a central server, location data may identify a particular person. Thus, the transmitted data must be guarded against re-identification and an un-trusted server. This paper overcomes limitations of previous works and provides a privacy preserving aggregation framework for distributed data streams. Individual location data is obfuscated to the server and just aggregates of k persons can be processed. This is ensured by use of Pailler's homomorphic encryption framework and Shamir's secret sharing procedure. In result we obtain anonymous unification of the data streams in an un-trusted environment.

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“…The sketching method uses count-min sketch algorithm to compress vehicular movement data and achieve compact communication. Although it ensures data reduction by preserving some important characteristics of the original data, the probability of information fidelity loss is more when sketching is applied with inconsistent and noisy data stream [46].…”

Section: Open Research Issuesmentioning

confidence: 99%

“…The authors in [46] utilized sketching algorithm called count-min sketch algorithm to compress vehicular movement data and achieved compact communication. The algorithm maps frequency counters to compressed vectors using hash tables.…”

Section: Compressionmentioning

confidence: 99%

“…On the other hand, wireless sensor networks (WSNs) are generating large data streams at massive scales. The spatiotemporal data compression algorithm proposed in [46] ensures efficient communication, transmission, and storage of data in WSNs-based big data environment. The proposed approach not only reduces the size of transmitted data but also ensures prolonged network lifetime.…”

Section: Compressionmentioning

confidence: 99%

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Big Data Reduction Methods: A Survey

et al. 2016

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Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.

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Privacy-Preserving Distributed Movement Data Aggregation

Cited by 25 publications

References 24 publications

FuturICT — The road towards ethical ICT

FuturICT — The road towards ethical ICT

Privacy Preserving Centralized Counting of Moving Objects

Big Data Reduction Methods: A Survey

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