Prior to the advent of big data, the following principles were of broad application in several regulations for the protection of personally identifiable information (PII) : Moreover, more than often, these data are not consciously supplied by the data subject (typically a consumer, citizen), but they are generated as a by-product of some transaction (e.g., browsing or purchasing items in an online store), or they are obtained by the service provider in return for some free service (for example, free email accounts, social networks) or as a natural requirement for some service (e.g., a GPS navigation system needs knowledge about the position of an individual to supply her with information on nearby traffic conditions).Īt the moment, there is not a clear view of the best strategy or strategies to protect privacy in big data. It should be taken into account that big data are all about gathering as many data as possible to extract knowledge from them (possibly in some innovative ways). The potential risk to privacy is one of the greatest downsides of big data. 6 summarizes the conclusions of our work. 4 and 5, respectively, we evaluate the two best-known privacy models, namely k-anonymity and differential privacy , in terms of the desirable properties for big data protection established in Sect. 3 we seek to determine the properties that a privacy model must have to be appropriate for big data privacy. Given that anonymization appears as the best option to mitigate that conflict, and since privacy models seem the soundest approach to anonymization, in Sect. 2, we examine the conflict between big data and the legal and ethical requirements in private data management. We next sketch the contributions and the structure of this paper. However, with big data this setting does not suffice any more. Existing privacy models have been mostly developed with a single static data set in mind. Privacy models usually depend on one or several parameters that determine how much disclosure risk is acceptable. Rather than determining the specific transformation that should be carried out on the original data, privacy models specify conditions that the data set must satisfy to keep disclosure risk under control. In recent years, several privacy models have been proposed. A common feature in all of them is that the original data set is kept secret and only a modified (anonymized) version of it is released. ![]() ![]() Several SDC techniques have been proposed to limit the disclosure risk in microdata releases. Statistical disclosure control (SDC, ) seeks to allow one to make useful inferences about subpopulations from a data set while at the same time preserving the privacy of the individuals that contributed their data. While in a different setting and scale, disclosure risk has long been a concern in the statistical and computer science communities, and several techniques for limiting such risk have been proposed. It was later discovered that she was pregnant but her father was still unaware of it. Some time later, a father complained to Target that her daughter, still at high school, had been sent coupons for baby clothes he asked whether they were encouraging her to get pregnant. The goal was to send discount coupons on several baby-related products as soon as possible with the aim to shape long-standing buying patterns to Target’s advantage. Target, a large retailer, created a model for pregnancy prediction. An illustrative case of this is reported in. The privacy of the individuals whose data are being collected and analyzed (often without their being aware of it) is increasingly at risk. While a valuable resource in many fields, there is an important side effect of big data. Data analysis is no longer only a matter of describing data or testing hypotheses, but also of generating (previously unavailable) knowledge out of the data. The amount and variety of data have made sophisticated data analyses possible. The large amount of data has put too much pressure on traditional structured data stores, and as result new technologies have appeared, such as Hadoop, NoSQL, MapReduce. ![]() Big data depart from previous data sets in several aspects such as volume, variety and velocity. Big data have become a reality in recent years: Data are being collected by a multitude of independent sources, and then they are fused and analyzed to generate knowledge.
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