Database Principles Programming And Performance Pdf
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- Database: Principles, programming, and performance
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Data Science From Scratch 2nd Edition Pdf Github
This paper explores the challenges raised by big data in privacy-preserving data management. First, we examine the conflicts raised by big data with respect to preexisting concepts of private data management, such as consent, purpose limitation, transparency and individual rights of access, rectification and erasure.
Anonymization appears as the best tool to mitigate such conflicts, and it is best implemented by adhering to a privacy model with precise privacy guarantees. 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. Big data depart from previous data sets in several aspects such as volume, variety and velocity. 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 [ 7 ].
The amount and variety of data have made sophisticated data analyses possible. 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.
While a valuable resource in many fields, there is an important side effect of big data. The privacy of the individuals whose data are being collected and analyzed often without their being aware of it is increasingly at risk. An illustrative case of this is reported in [ 9 ]. Target, a large retailer, created a model for pregnancy prediction. 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.
It was later discovered that she was pregnant but her father was still unaware of it. 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. Statistical disclosure control SDC, [ 14 ] 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.
Several SDC techniques have been proposed to limit the disclosure risk in microdata releases. 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. In recent years, several privacy models have been proposed. 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.
Privacy models usually depend on one or several parameters that determine how much disclosure risk is acceptable. Existing privacy models have been mostly developed with a single static data set in mind. However, with big data this setting does not suffice any more.
We next sketch the contributions and the structure of this paper. In Sect. Given that anonymization appears as the best option to mitigate that conflict, and since privacy models seem the soundest approach to anonymization, in Sect. In Sects.
Finally, Sect. The potential risk to privacy is one of the greatest downsides of 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. 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.
At the moment, there is not a clear view of the best strategy or strategies to protect privacy in big data. 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 [ 6 ]:. The consent given by the subject must be simple, specific, informed and explicit. Purpose limitation. The purpose of the data collection must be legitimate and specified before the collection.
Necessity and data minimization. Only the data needed for the specific purpose should be collected. Furthermore, the data must be kept only for as long as necessary. Transparency and openness. Subjects need to get information about data collection and processing in a way they can understand.
Individual rights. Subjects should be given access to the data on them, as well as the possibility to rectify or even erase such data right to be forgotten.
Information security. The collected data must be protected against unauthorized access, processing, manipulation, loss or destruction. Privacy must be built-in from the start rather than added later.
Without anonymization, several potential conflicts appear between the above principles and the purpose of big data:. Big data are often used secondarily for purposes not even known at the time of collection. If the purpose of data collection is not clear, consent cannot be obtained.
Without purpose limitation and consent, lawfulness is dubious. Big data result precisely from accumulating data for potential use. Individual subjects do not even know which data are stored on them or even who holds data on them. Accessing, rectifying or erasing the data is therefore infeasible for them.
Compliance does not hold and hence it cannot be demonstrated. Given the above conflicts between privacy principles and big data, it has been argued that, in order to avoid hampering technological development, privacy protection should focus only on potentially privacy-harming uses of the data rather than on data collection or even allow for self-regulation. In the opposite camp, it has been also argued that it is the mere collection of the data that triggers any potential privacy breaches.
Data breach. This may happen as a result of aggressive hacking or insufficient security measures. The more data are collected, the more appealing they become for an attacker.
Changes in company practices. Anonymization techniques are a possible solution to overcome the conflicts between privacy principles and big data. As the privacy principles above refer to PII, once the data have been anonymized they may be viewed as being no longer PII and hence one may claim that principles no longer apply to them. However, anonymization techniques face some difficulties when applied to big data.
On one side, too little anonymization e. This becomes more problematic with big data because, as the amount and variety of data about an individual accumulates, re-identification becomes more plausible. On the other side, too strong an anonymization may prevent linking data on the same individual subject or on similar individual subjects that come from different sources and, thus, thwart many of the potential benefits of big data.
While it is obvious that there are some tensions between big data and data anonymization, we should not rule out the latter. Admittedly, it is true that anonymization can hamper some of the uses of big data mainly those uses targeting a specific individual , but anonymized data still enable most analyses in which the target is a sufficiently large community or the entire population.
Yet, in general they do not specify any mechanism to assess what is the disclosure risk remaining in the transformed data. In this sense, privacy models seem more appealing. The reality, however, is that most privacy models have been designed to protect a single static original data set and, thus, there are several important limitations in their application to big data settings.
The three characteristics often mentioned as distinctive of big data are volume, variety and velocity. Volume refers to the fact that the amount of data is are subject to analysis is large. Variety refers to the fact that big data consist of heterogeneous types of data extracted and fused from several different sources.
Velocity refers to the speed of generation and the processing of the data. Certainly, not all the above properties need to concur for the name big data to be used, but at least some of them are required.
For a privacy model to be usable in a big data environment, it must cope well with volume, variety and velocity. To determine the suitability of a privacy model for big data, we look at the extent to which it satisfies the following three properties:.
A privacy model is composable if the privacy guarantees of the model are preserved possibly to a limited extent after repeated independent application of the privacy model. From the opposite perspective, a privacy model is not composable if multiple independently data releases, each of them satisfying the requirements of the privacy model, may result in a privacy breach.
Computational cost. The computational cost measures the amount of work needed to transform the original data set into a data set that satisfies the requirements of the privacy model. We have previously mentioned that, usually, there is a variety of SDC techniques that can be employed to satisfy the requirements of the privacy model.
Thus, the computational cost depends on the particular SDC technique selected. When evaluating the cost of a privacy model, we will consider the most common approaches. In big data, information about an individual is gathered from several independent sources. Hence, the ability to link records that belong to the same or a similar individual is central in big data creation.
With privacy protection in mind, the data collected by a given source should be anonymized before being released. However, this independent anonymization may limit the data fusion capabilities, thereby severely restricting the range of analyses that can be performed on the data and, consequently, the knowledge that can be generated from them.
The amount of linkability compatible with a privacy model determines whether and how an analyst can link data independently anonymized under that model that correspond to the same individual. Notice that, when linking records belonging to the same individual, we are increasing the information about this individual. This is a privacy threat, and thus, the accuracy of the linkages should be less in anonymized data sets than in original data sets.
All of the above properties of a privacy model seem to be important to effectively deal with big data. We next discuss the importance of each property in some detail. Composability is essential for the privacy guarantees of a model to be meaningful in the big data context. In big data, the process of data collection is not centralized, but distributed among several data sources.
If one of the data collectors is concerned about privacy and decides to use a specific privacy model, the privacy guarantees of the selected model should be preserved to some extent after the fusion of the data. Composability can be evaluated between data releases that satisfy the same privacy model, between data releases that satisfy different privacy models and between a data release that satisfies a privacy model and non-anonymized data. In this paper, we evaluate only composability between data releases that satisfy the same privacy model.
Performance Testing Tutorial: What is, Types, Metrics & Example
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Database: Principles, programming, and performance
Database: Principles Programming Performance provides an introduction to the fundamental principles of database systems. This book focuses on database programming and the relationships between principles, programming, and performance. Organized into 10 chapters, this book begins with an overview of database design principles and presents a comprehensive introduction to the concepts used by a DBA. This text then provides grounding in many abstract concepts of the relational model. Other chapters introduce SQL, describing its capabilities and covering the statements and functions of the programming language.
Learn the basics of. This means plain-English explanations and no coding experience required. The main form is now closed, please fill out the late registration form. Data Science from scratch is must for the beginners who want an overview and theoretical concepts on python, data visualization, data science , ML ,neural networks and so on. For example, we can store a list of items having the same data-type using the array data structure.
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Written in English. Database Modeling and Design, Fourth Edition, the extensively revised edition of the classic logical database design reference, explains how you can model and design your database application in consideration of new technology or new business needs. Get this from a library! Database: principles, programming, performance. Date, A.
Performance Testing is a software testing process used for testing the speed, response time, stability, reliability, scalability and resource usage of a software application under particular workload. The main purpose of performance testing is to identify and eliminate the performance bottlenecks in the software application. Features and Functionality supported by a software system is not the only concern.
This paper explores the challenges raised by big data in privacy-preserving data management. First, we examine the conflicts raised by big data with respect to preexisting concepts of private data management, such as consent, purpose limitation, transparency and individual rights of access, rectification and erasure. Anonymization appears as the best tool to mitigate such conflicts, and it is best implemented by adhering to a privacy model with precise privacy guarantees. 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.