By Linnet Taylor, Luciano Floridi, Bart van der Sloot
The objective of the e-book is to give the newest examine at the new demanding situations of information applied sciences. it is going to provide an outline of the social, moral and felony difficulties posed by way of team profiling, titanic facts and predictive research and of the various methods and techniques that may be used to handle them. In doing so, it is going to aid the reader to realize a greater clutch of the moral and criminal conundrums posed by means of workforce profiling. the amount first maps the present and rising makes use of of latest information applied sciences and clarifies the guarantees and risks of workforce profiling in actual lifestyles occasions. It then balances this with an research of the way some distance the present felony paradigm provides workforce rights to privateness and information defense, and discusses attainable routes to addressing those difficulties. eventually, an afterword gathers the conclusions reached by means of the several authors and talk about destiny views on regulating new info technologies.
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Extra info for Group Privacy: New Challenges of Data Technologies
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2014. The uses of big data in cities. Big Data 2(1): 12–22. Ziemlicki. 2012. Data for development: The d4d challenge on mobile phone data. 0137. J. 1978. Individual and group privacy. New Brunswick: Transaction Publishers. E. 2012. Inferring patterns of internal migration from mobile phone call records: Evidence from Rwanda. Information Technology for Development 18(2): 107–125. Kondepudi. 2014. Probabilistic inference of unknown locations: Exploiting collective behavior when individual data is scarce.
While watching militias form and move in African conflict zones, the UN can also see the activities of the entire population, and while seeking particular consumers in remote areas, commercial drones will inevitably pick up other activities, locations and movements. These datasets, like other big data about human activities, will almost inevitably be subject to function creep. They will make it possible to identify, sort, categorise and predict with relation to populations who often have very limited access to their own governments, or which may want to stay anonymous if they are endangered by being recognised.
More recently new data sources where this kind of ground truth is not available – or at least not until the epidemic has passed – have been used in higher-stakes scenarios to track a variety of life-threatening diseases in developing countries, notably cholera in the wake of the 2010 Haiti earthquake (Bengtsson et al. 2011) and the use of network analysis to track malaria transmission (Tatem et al. 2009). This kind of tracking via data is particularly useful in the developing world, where public health infrastructure and capacity are often lacking and where the new data analytics can provide an unprecedented real-time element to authorities looking to control outbreaks of diseases.