3.3 Failure to Secure Sensitive Data Many researchers have been concerned about the security of sensitive data. There are a lot of improvement and evolution of technologies in internal auditing. First of all‚ sensitive data is information that must be protected from unauthorized access to safeguard the privacy or security of an individual or organization. There are three main types of sensitive data such as personal information‚ business information and classified information. Sensitive personally
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allow the researchers to analyze empirical data needed to interpret the findings and draw conclusions based on the results of the research. According to Portney and Watkins (2009)‚ all studies require a description of subjects and responses that are obtained through measuring central tendency‚ so all studies use descriptive statistics to present an appropriate use of statistical tests and the validity of data interpretation. Although descriptive statistics do not allow general conclusions and allow
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http://hdl.handle.net/2451/31553 Data Science and Prediction Vasant Dhar Professor‚ Stern School of Business Director‚ Center for Digital Economy Research March 29‚ 2012 Abstract The use of the term “Data Science” is becoming increasingly common along with “Big Data.” What does Data Science mean? Is there something unique about it? What skills should a “data scientist” possess to be productive in the emerging digital age characterized by a deluge of data? What are the implications for business
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educate a group or individual can also be used against such groups or individuals. Industrial espionage among highly competitive businesses often requires that extensive security measures be put into place. And‚ those who wish to exercise their personal freedom‚ outside of the oppressive nature of governments‚ may also wish to encrypt certain information to avoid suffering the penalties of going against the wishes of those who attempt to control. Still‚ the methods of data encryption and decryption
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learning. Learning is the process each person absorbs and retains information and/or skills (2000). It is a process based on newly acquired knowledge and past personal experience. Both newly acquired knowledge and personal experience lead consumers to future behavior in similar situations. Among all researches and studies of consumer learning‚ there’re two major branches: behavioral learning theories and cognitive learning theories. Behavioral Learning Theories Behavioral learning theories are sometimes
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number of articles on “big data”. Examine the subject and discuss how it is relevant to companies like Tesco. Introduction to Big Data In 2012‚ the concept of ‘Big Data’ became widely debated issue as we now live in the information and Internet based era where everyday up to 2.5 Exabyte (=1 billion GB) of data were created‚ and the number is doubling every 40 months (Brynjolfsson & McAfee‚ 2012). According to a recent research from IBM (2012)‚ 90 percent of the data in the world has been
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An Oracle White Paper July 2010 Data Masking Best Practices Oracle White Paper—Data Masking Best Practices Executive Overview ........................................................................... 1 Introduction ....................................................................................... 1 The Challenges of Masking Data ....................................................... 2 Implementing Data Masking .............................................................. 2
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Ensuring Data Storage Security in Cloud Computing Cong Wang‚ Qian Wang‚ and Kui Ren Department of ECE Illinois Institute of Technology Email: {cwang‚ qwang‚ kren}@ece.iit.edu Wenjing Lou Department of ECE Worcester Polytechnic Institute Email: wjlou@ece.wpi.edu Abstract—Cloud Computing has been envisioned as the nextgeneration architecture of IT Enterprise. In contrast to traditional solutions‚ where the IT services are under proper physical‚ logical and personnel controls‚ Cloud Computing
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IT433 Data Warehousing and Data Mining — Data Preprocessing — 1 Data Preprocessing • Why preprocess the data? • Descriptive data summarization • Data cleaning • Data integration and transformation • Data reduction • Discretization and concept hierarchy generation • Summary 2 Why Data Preprocessing? • Data in the real world is dirty – incomplete: lacking attribute values‚ lacking certain attributes of interest‚ or containing only aggregate data • e.g.‚ occupation=“ ”
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Database Models s L11: ER Diagrams Kieran Herley Summary The database design process and the role of ER modelling in same. Basic constituents of ER diagrams: entities‚ relationships and attributes. While designing database it’s handy to have notation to “sketch” schema s Two common diagrammatic notations: x x s UML diagrams (Universal Modelling Language) ER diagrams (Entity Relationship) → cs1106 choice Captures the information requirements of our database: what data it con and how these
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