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Investment Decisions and Analyses Dr. John Saymansky Research Assistant Professor

Investment Decisions and Analyses Dr. John Saymansky Research Assistant Professor. Personal Corporate Public. Questions. Personal How do you decide on what to do with your discretionary income? Corporate What do you do with profits?. 4 Key Concepts. Time Value of Money

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Investment Decisions and Analyses Dr. John Saymansky Research Assistant Professor

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  1. Investment Decisions and AnalysesDr. John SaymanskyResearch Assistant Professor Personal Corporate Public

  2. Questions • Personal • How do you decide on what to do with your discretionary income? • Corporate • What do you do with profits?

  3. 4 Key Concepts • Time Value of Money • Implications of Taxes • Relationship Between Risk and Returns • Inflation

  4. CS 665 Anonymization of Statistical Databases by Daniel Painter

  5. The world we live in… • Increased digitalization of records • Digital storage trend will continue to grow in the future

  6. Statistical Databases • Variation in meaning • What is a “true” statistical database? • What people tend to really mean…

  7. But I don’t want to die! • We tend to not change unless they have a reason to do so • It is important to understand that there is a problem • Examples?

  8. So, what problems do we have? • Data inference by an attacker • Multiple sources of distributed data

  9. Example one: Medical records1 • How do we infer data? • Consider a patient’s record and diagnosis • Age, gender, zip code • Combine this with publicly available information

  10. Example two: Hanging Chad • So, you buy some things off of the internet • Amazon • Newegg • Ebay • Chad’s Lawn and Garden

  11. Real life example: The Bus

  12. Jerome’s address + Allegheny County Database…

  13. From his parcel ID I can get…

  14. It continues to…

  15. Finally…

  16. How do we keep our data safe? • K-anonymized data • A k-anonymized dataset has the property that each record is indistinguishable from at least k-1 others. • Protect sensitive data

  17. More examples (a K-2 anonymization):

  18. Techniques used in this anonymization • Supression • Generalization

  19. Microaggregation • Partition records into groups that are similar

  20. A much better way to make data safe… • Intelligent database design • Separate sensitive data from data people can view • ENCRYPT!!!

  21. What needs to be done & questions • Make people aware of statistical database anonymization • Design databases to incorporate DA • Standards for databases containing personal information

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