Privacy and Big Data: Exploring the Implications

1. INLS 151monnov 2

2. Presentation options: Wed, Nov 18; Mon Nov 23; Mon Nov 30

3. Pecha kucha presentation https://www.youtube.com/watch?v=wGaCLWaZLI4 20 slides, 20 seconds each = about 7 minutes

4. Short presentation format tips • Bait audience,catchy hook, why should we care? • Tell us what you are going to tell us… • Relevant images (graphs, charts, photos, clip art) • Main point in a short phrase • Script + practice, practice, practice • Cite your sources (corresponding slide or at end) • Energy, enthusiasm, & eye contact • Do enough prep that you are comfortable • Anticipate & prepare for questions

5. What do we mean by privacy? • Louis Brandeis (1890) • “right to be left alone” • protection from institutional threat: government, press • Supreme Court justice • Alan Westin (1967) • legal scholar, modern right to privacy • “right to control, edit, manage, and delete information about themselves and decide when, how, and to what extent information is communicated to others” Privacy and Freedom, 1967

6. Privacy vs. security Privacy: what information goes where? Security: protection against unauthorized access • Security helps enforce privacy policies • Can be at odds with each other • e.g., invasive screening to make us more “secure” against terrorism

7. DEFINITION: “Big Data” Big Data is used in the singular and refers to a collection of data sets so large and complex, it’s impossible to process them with the usual databases and tools. Because of its size and associated numbers, Big Data is hard to capture, store, search, share, analyze and visualize. The phenomenon came about in recent years due to the sheer amount of machine data being generated today – thanks to mobile devices, tracking systems, RFID, sensor networks, social networks, Internet searches, automated record keeping, video archives, e-commerce, etc. – coupled with the additional information derived by analyzing all this information, which on its own creates another enormous data set. Companies pursue Big Data because it can be revelatory in spotting business trends, improving research quality, and gaining insights in a variety of fields, from IT to medicine to law enforcement and everything in between and beyond.

8. Massive Messy Data • Big Data analysis requires collecting • massive amounts of • messy data • Messy data: The data is not in a uniform format as one would see in traditional database, it is not annotated (semantically tagged) • technological breakthroughs allow us to find ways to manipulate and analyze such data. • Massive amounts: think of every tweet ever tweeted. They are all in the Library of Congress(a project that may be failing, imagine 400 million tweets a day in 2013.

9. Patterns We Would Not Notice • Big Data analytics can reveal important patterns that would otherwise go unnoticed. • Taking the antidepressant Paxil together with the anti-cholesterol drug Pravachol could result in diabetic blood sugar levels. Discovered by • (1) using a symptomatic footprint characteristic of very high blood sugar levels obtained by analyzing thirty years of reports in an FDA database, and • (2) then finding that footprint in the Bing searches using an algorithm that detected statistically significant correlations. People taking both drugs also tended to enter search terms (“fatigue” and “headache,” for example) that constitute the symptomatic footprint.

10. DEFINITION: “Cookie” A cookie is a small amount of data generated by a website and saved by your browser. Its purpose is to remember information about you, similar to a preference file created by a software application. Cookies are also used to store user preferences for a specific site. For example, search engines like Google or Bing store your searches. Financial websites sometimes use cookies to store recently viewed stock quotes. If a website needs to store a lot of personal information, it may use a cookie to remember who you are, but will load the information from its server. Browser cookies come in two different flavors: "session" and "persistent." Session cookies are temporary and are deleted when the browser is closed. These types of cookies are often used by e-commerce sites to store items placed in your ‘shopping cart,’ and can serve many other purposes as well. Persistent cookies are designed to store data for an extended period of time. Each persistent cookie is created with an expiration date, which may be anywhere from a few days to several years in the future. Once the expiration date is reached, the cookie is automatically deleted.

11. DEFINITION: “RFID” RFID stands for RadioFrequencyIDentification, a technology that uses tiny computer chips smaller than a grain of sand to track items at a distance. RFID chips have been hidden in the packaging of Gillette razor products and in other products you might buy at a local Wal-Mart, Target, or Costco - and they are already being used to “spy” on people. Each tiny chip is hooked up to an antenna that picks up electromagnetic energy beamed at it from a reader device. When it picks up the energy, the chip sends back its unique identification number to the reader device, allowing the item to be remotely identified. These chips can beam back information anywhere from a couple of inches to up to 20 or 30 feet away. Shown at left is a magnified image of actual RFID tag found in Gillette Mach3 razor blades. The chip appears as the tiny black square. The coil of wires surrounding the chip is the antenna, which transmits your information to a reader device, which can be located anywhere!

12. DEFINITION: “RFID” (continued) This technology is rapidly evolving and becoming more sophisticated. Now RFID chips can even be printed, meaning the dot on a printed letter "i" could be used to track you. Companies are even experimenting with making the product packages themselves serve as antennas. RFID chips can be well hidden. For example they can be sewn into the seams of clothes, sandwiched between layers of cardboard, and molded into plastic or rubber. Unlike a bar code, these chipscan be read from a distance, right through your clothes, wallet, backpack or purse -- without your knowledge or consent -- by anybody with the right reader device.  Many large corporations, including Philip Morris, Procter and Gamble, and Wal-Mart, have begun experimenting with RFID chip technology and have recently placed an order for up to 500 million RFID tags from a company called Alien Technology. 

13. Speaking of miniaturization…..(a slight digression) • Smartphones and tablets outsold desktop and laptop computers in 2014; 170 million smartphones in U.S. 2014* • The phone in your pocket has more programmable memory, more storage and more capability than several large IBM computers. • It takes dozens of microprocessors running 100 million lines of code to get a premium car out of the driveway, and this software is only going to get more complex. In fact, the cost of software and electronics accounts for 30-40% of the price. *Statistia

14. What is collecting all this data? Web Browsers Search Engines Google’s Microsoft’s Internet Explorer Mozilla’s FireFox Microsoft (Non-profit foundation, used to be Netscape) Google’s Chrome Yahoo’s Apple’s Safari IAC Search’s Time-Warner’s AOL Explorer

15. What is collecting all this data? Smartphones & Apps Tablet Computers & Apps Apple’s iPad Apple’s iPhone (Apple O/S) Samsung, HTC. Nokia, Motorola (Android O/S) Samsung’s Galaxy RIM Corp’s Blackberry (BlackBerry O/S) Amazon’s Kindle Fire

16. What is collecting all this data? Games Boxes and GPS Systems Internet Service Providers

17. What is collecting all this data? Smart TVs and Blu-Ray Players with built-in Internet connectivity Movie Rental Sites

18. What is collecting all this data? Hospitals & Other Medical Systems Banking & Phone Systems Can you hear me now? (Heh heh heh!)

19. What is collecting all this data? A real pain in the apps! What are they collecting? Restaurant reservations (Open Table) Weather in L.A. in 3 days (Weather+) Side effects of medications (MedWatcher) 3-star hotels in New Orleans (Priceline) Which PC should I buy and where (PriceCheck)

20. Who is collecting all of this data? Government Agencies Big Pharmaceutical Companies

21. Who is collecting all this data? Consumer Products Companies Big Box Stores

22. Who is collecting what? Credit Card Companies What data are they getting? Airline ticket Restaurant check Grocery Bill Hotel Bill

23. Why are they collecting all this data? Target Marketing Targeted Information To know what you need before you even know you need it based on past purchasing habits! To notify you of your expiring driver’s license or credit cards or last refill on a Rx, etc. To give you turn-by-turn directions to a shelter in case of emergency. • To send you catalogs for exactly the merchandise you typically purchase. • To suggest medications that precisely match your medical history. • To “push” television channels to your set instead of your “pulling” them in. • To send advertisements on those channels just for you!

24. Examples of big data….. Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data — the equivalent of 167 times the information contained in all the books in the US Library of Congress. FICO Credit Card Fraud Detection System protects 2.1 billion active accounts world-wide. The volume of business data worldwide, across all companies, doubles every 1.2 years, according to estimates

25. Examples of Big Data With a smart meter, a utility company goes from collecting one data point a month per customer (using a meter reader in a truck or car) to receiving 3,000 data points for each customer each month, while smart meters send usage information up to four times an hour. One small Midwestern utility is using smart meter data to structure conservation programs that analyze existing usage to forecast future use, price usage based on demand and share that information with customers who might decide to forestall doing that load of wash until they can pay for it at the nonpeak price.

26. Examples of Big Data Global position satellite technology now allows trucking firms to track their trucks - and the merchandise inside them. Practically anything you can attach an RFID tag to can be tracked. How a company uses that information – to re-route trucks to create efficient routes, alert customers to deliveries, and forecast and price services – depends on the ability to manage and analyze data effectively.

27. Big Brother Needs Big Data In March 2012, the Obama Administration announced the Big Data Research and Development Initiative, $200 million in new R&D investments, which will explore how Big Data could be used to address important problems facing the government. The initiative was composed of 84 different Big Data programs spread across six departments. http://tinyurl.com/85oytkj

28. What are some impacts of Big Data? • Decisions like your credit score and your insurance rates may be based on the analysis of big data, for good or bad.  • After Haiti’s 2010 earthquake, Columbia University tracked the movements of 2 million refugees by the SIM cards in their cell phones and were able to determine where health risks would likely develop.

29. Is Big Data good or bad for consumers? • How would you feel about paying more for the same product than the person checking out in front of you? • The real challenge: are you willing to get better value and more innovation for some loss of privacy? • Since there is no way to stop the accumulation of Big Data, should its use be regulated by the Federal government?

30. “Right to be forgotten” Through 12:20 Via European courts: residents can ask corporations like Google to delete those unflattering posts, photos and other online material from online search results YES, right to be forgotten NO, right to be forgotten Andrew McLaughlin is CEO of Digg and Instapaper and a partner at Betaworks. From 2009-11, he was a member of Obama's senior White House staff. Former director of global public policy at Google. Jonathan Zittrain is the George Bemis Professor of Law at Harvard Law School and the Kennedy School of Government Paul F. Nemitz is the director for fundamental rights and union citizenship of the European Commission's Directorate General for Justice and Consumers. Eric Posner is the Kirkland and Ellis Distinguished Service Professor of Law at the University of Chicago

31. How Can You Avoid Big Data? • Pay cash for everything! • Never go online! • Don’t use a telephone! • Don’t use Kroger or Harris Teeter cards! • Don’t fill any prescriptions! • Never leave your house!