KV Enterprise Business Intelligence: Big Data and Privacy
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| Typ | KV |
|---|---|
| Titel | Enterprise Business Intelligence: Big Data and Privacy |
| Klasse | ASSWINBDAPK15 |
| Semesterstunden | 4 SSt. (6 ECTS) |
| Semester | SS |
| Ziele | COURSE DESCRIPTION AND LEARNING OBJECTIVES: The course covers the concept of "big data" and its management through technology. It elucidates that ensuring protecting personal privacy becomes harder as information is multiplied and shared ever more widely around the world. Information regarding individuals’ health, location, and online activity is exposed to scrutiny, raising concerns about profiling, discrimination, exclusion, and loss of control. The course is designed for graduate students and upon successful completion of this course students should be adept in the following areas: (1) On the conceptual side: (a) Understand big data and its role as an instrument to guide business operations towards achieving strategic objectives, (b) Study the cost-benefit analysis of big data as it is utilized to disseminate knowledge and strengthen social cohesion while raising concerns regarding the extent to which "user privacy' can be ensured", (c) Examine the state of privacy protection via technology and policies in Austria. (2) On the technical side: (a) Study Privacy Invasive Technologies (PIT) and Privacy Enhancing Technologies (PET), (b) Students will get a chance to get certified in Hadoop (Big Data Application) and fairly competent in using Tableau Software (BI application), (c) Time permitting, we will study MapReduced. |
| Inhalt | COURSE FORMAT: The course will consist of lecture series, class discussions, Lab exercises, group assignments, and a final submission that could be a technology based project or a term paper. To simulate the real world work environment, students will work in groups and make class presentations on the topics assigned by the instructor. The course is highly interactive; students are required to study the course materials provided by the instructor in advance and contribute to class discussions and presentations in the class. Lab exercises will be assigned and students will work on the existing Privacy Enhancing Technologies (PET) and Privacy Invasive Technologies (PIT) and their implementations. The extent of PET deployment as well as bylaws regarding data privacy protection in Austria will be examined. It is imperative that students come to class prepared: reading before each class and be prepared for discussing the revenant topics. Hand-outs will be given for lab exercises and students are expected to have studies them in advance in order to be ready to complete and submit their work at the end of each session- there would exceptions when students can complete lab exercises afterwards. Please note the syllabus materials are open to negotiation- since I knew nothing about the students, we will spend the first class to come up with work load, submission criteria, etc, that is doable, challenging, and hopefully enjoyable for us as a whole. |
| Hinweise | Detaillierte Informationen |
| Voraussetzungen | BACKGROUND: In a digital setting, such as the Internet, there are a wide variety of privacy threats, ranging from tracking user online activities, to mass marketing based on the retrieval of personal information, to the distribution of information on dangerous technologies used for, e.g., acts of terror. In the last few decades numerous privacy-enhancing technologies have been developed with mixed results- some have been successful while others have seen little adoption despite much hype and promise. We will study privacy technologies, their uses and limitations, the reasons for their success and failure, and think critically about their place in society. More broadly, we will try to understand the Privacy Paradox: Conflicting values in the age of Big Data. There is a call for restricting the flow of data or ensuring that the flow of the information on the Internet is managed appropriately. This is easier said than done because the subject of privacy in relation to information technology is deeply problematic; its definition, benefits, harms, and its conceptual morass have been debated and are notoriously controversial. Free flow of data on the Internet diminishes control over personal information hence provoking anxiety and resistance to the concept of big data. But, big data also promotes intellectual development, health and well-being, and democracy- should it be feared and avoided or accepted and even celebrated? Let’s find put. |
| Anrechenbarkeit | Master WIN (Spezialkompetenz WIN), Master Webwissenschaften (nähere Infos bei DDr. Höller) |