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Privacy and Cyberspace

Privacy and Cyberspace

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Privacy and Cyberspace

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  1. Privacy and Cyberspace • Are privacy issues unique to cybertechnology? • Four characteristics worth noting: • The amount of personal information that can be gathered using cybertechnology. • The speed at which personal information can be transmitted using cybertechnology. • The duration of time in which the information can be retained because of cybertechnology. • The kind of information that can now be transferred because of cybertechnology.

  2. What is Personal Privacy • Privacy is a concept that is neither clearly understood nor easily defined. • Sometimes we speak of one’s privacy as something that has been: • "lost," • "diminished," • "intruded upon," • "invaded," • "violated," • "breached," and so forth.

  3. What is Privacy (continued)? • Privacy is sometimes viewed as an "all-or-nothing" concept – that is, something that one either has (totally) or does not have. • At other times, privacy is viewed as something that can be diminished. • For example, as a repository of personal information that can be eroded gradually.

  4. Table 5-1: Three Theories of Privacy

  5. A Comprehensive Account of Privacy • Moor (1997) has introduced a theory of privacy that incorporates important elements of the non-intrusion, non-interference, and informational views of privacy. • According to Moor: • an individual has privacy in a situation if in that particular situation the individual is protected from intrusion, interference, and information access by others. [Italics Added]

  6. Moor’s Theory of Privacy (continued) • An important aspect in this definition is Moor's notion of a situation. • A situation is left deliberately broad so that it can apply to a range of contexts or "zones.“ • Situations can be "declared private" in a normative sense. • For example, a situation can be an "activity," a "relationship," or the "storage and access of information" in a computer or on the Internet.

  7. Moor’s Privacy Theory (continued) • Moor’s distinction between naturally private and normatively private situations enables us to differentiate between the conditions required for: • (a)having privacy (in a descriptive sense); • (b) having a right to privacy. • With this distinction we can differentiate between a: • loss of privacy; • violation of privacy.

  8. Two Scenarios • Scenario 1: Someone walks into the computer lab and sees you using a computer. • Your privacy is lost but not violated. • Scenario 2: Someone peeps through the keyhole of your apartment door and sees you using a computer. • Your privacy is not only lost but is violated.

  9. Why is Privacy Important? • What kind of value is privacy? • Is it one that is universally valued? • Is privacy valued mainly in Western industrialized societies, where greater importance is placed on individuals? • Is privacy something that is valued for its own sake – i.e., an intrinsic value? • Is it valued as a means to an end, in which case it has only instrumental worth?

  10. Privacy as a Universal Value • Not valued the same in all cultures. • Less valued in non-Western nations and in rural societies. • Less valued in some democratic societies (such as Israel) where security and safety are important. • Has at least some value in all societies.

  11. Is Privacy an Intrinsic or Instrumental Value? • Not valued for its own sake. • But is more than an instrumental value in the sense that it is necessary (rather than merely contingent) for achieving important human ends. • Fried – privacy is necessary for human ends such as trust and friendship. • Moor – privacy is an expression of the core value security.

  12. Privacy as an Important Social Value • Privacy is important for a diversity of relationships (from intimate to casual). • It is important for democracy. • Privacy is an important social, as well as an individual, value. • Regan (1995) – we need to understand the importance of privacy as a social value.

  13. Three Ways Privacy is Threat- ened by Cybertechnology? • (A) data-gathering techniques used to collect and record personal information, often without the knowledge and consent of users. • (B) data-exchanging techniques used to transfer and exchange personal data across and between computer databases, typically without the knowledge and consent of users. • (C) data-mining techniques used to search for patterns implicit in large databases in order to generate consumer profiles based on behavioral patterns discovered in certain groups.

  14. Gathering Personal Data • Personal data has been gathered since Roman times (census data). • “Dataveillance” – a term coined by Roger Clarke to capture two techniques made possible by computer technology: • (a) the surveillance (data-monitoring): • (b) data-recording.

  15. Dataveillance (Continued) • Video cameras monitor an individual's physical movements – when they shop at certain department stores. • Some motorists are now subject to new schemes of highway surveillance while driving in their motor vehicles, because of new forms of scanning devices such as E-ZPASS. • Even the number of "clickstreams" – key strokes and mouse clicks – entered by a Web site visitor can be monitored and recorded.

  16. Internet Cookies • “Cookies” are files that Web sites send to and retrieve from the computer systems of Web users. • Cookies technology enables Web site owners to collect certain kinds of data about the users who access their sites. • Because of "cookies technology," information about an individual's on-line browsing preferences can be "captured" whenever a person visits a Web site.

  17. Cookies (Continued) • The data recorded (via cookies) about the user is then stored on a file placed on the hard drive of the user's computer system. • No other data-gathering mechanism actually stores the data it collects on the user’s computer. • The information can then be retrieved from the user's system and resubmitted to a Web site the next time the user accesses that site. • The exchange of data typically occurs without a user's knowledge and consent.

  18. Can Cookies be Defended? • Web sites that use cookies maintain that they are performing a service for repeat users of a Web site by customizing a user's means of information retrieval. • They also point out that, because of cookies, they are able to provide a user with a list of preferences for future visits to that Web site.

  19. Arguments Against Cookies • Privacy advocates argue that activities involving the monitoring and recording an individual's activities while visiting a Web site and the subsequent downloading of that information onto a user's PC (without informing the user), violate privacy. • They also point out that information gathered about a user via cookies can eventually be acquired by on-line advertising agencies, who could then target that user for on-line ads.

  20. Computerized Merging and Matching Operations • Computer merging is a technique of extracting information from two or more unrelated databases, which contain data about some individual or group of individuals, and incorporating it into a composite file. • Computer merging occurs whenever two or more disparate pieces of information contained in separate databases are combined.

  21. Computer Merging • Consider a scenario in which you voluntarily give information about yourself to three different organizations. • First, you give information about your income and credit history to a lending institution in order to secure a loan. • You next give information about your age and medical history to an insurance company to purchase life insurance. • You then give information about your views on certain social issues to a political organization you wish to join.

  22. Computer Merging (continued) • Each organization has a legitimate need for information to make decisions about you. • Insurance companies have a legitimate need to know about your age and medical history before agreeing to sell you life insurance. • Lending institutions have a legitimate need to know information about your income and credit history before agreeing to lend you money to purchase a house or a car.

  23. Computer Merging (continued) • Suppose that, without your knowledge and consent, information about you contained in the insurance company's database is merged with information about you that resided in the lending institution's database or in the political organization's database. • You voluntarily gave certain information about yourself to three different organizations. • You authorized each organization to have the specific information you voluntary granted. • However, it does not follow that you thereby authorized any one organization to have some combination of that information.

  24. Computer Merging (continued) • Case Illustration • Double-Click, an on-line advertising company attempted to purchase Abacus, Inc. an off-line database company. • Double-Click would have been able to merge on-line and off-line records.

  25. Computer Matching • Computer matching is a technique that involves the cross checking of information in two or more databases that are typically unrelated in order to produces certain "matching records" or "hits." • Matching or cross-referencing records in two or more databases in order to generate one or more hits is used for the express purpose of creating a new file, which typically contains a list of potential law violators.

  26. Computer Matching (continued) • In federal and state government applications, computerized matching has been used by various agencies and departments to identify: • potential law violators; • individuals who have actually broken the law or who are suspected of having broken the law (welfare cheats, deadbeat parents, etc.).

  27. Computer Matching (continued) • A scenario could be federal income tax records matched against state motor vehicle registration (looking for low income and expensive automobiles). • Consider an analogy in physical space in which your mail in monitored and secretly matched or opened by authorities.

  28. Computer Matching (continued) • Those who defend matching argue: • If you have nothing to hide, you have nothing to worry about. • Another argument is: • Privacy is a legal right. • Legal rights are not absolute. • When one violates the law (i.e., commits a crime), one forfeits one's legal rights. • Therefore, criminals have forfeited their right to privacy.

  29. Computer Matching (continued) • Case illustration involving biometrics: • At Super Bowl XXXV in January 2001, a facial-recognition technology was used to scan the faces of individuals entering the stadium. • The digitized facial images were then instantly matched against images contained in a centralized database of suspected criminals and terrorists. • This practice was, at the time, criticized by many civil-liberties proponents.

  30. Data Mining • Data mining involves the indirect gathering of personal information through an analysis of implicit patterns discoverable in data. • Data-mining activities can generate new and sometimes non-obvious classifications or categories. • Individuals whose data is mined could become identified with or linked to certain newly created groups that they might never have imagined to exist.

  31. Data Mining (Continued) • Current privacy laws offer individuals no protection regarding information about them that is acquired through data-mining activities is subsequently used. • Important decisions can be made about those individuals based on the patterns found in the mined personal data. • So some uses of data-mining technology raise special concerns for personal privacy.

  32. Data Mining (Continued) • Unlike personal data that resides in explicit records in databases, information acquired about persons via data mining is often derived fromimplicit patterns in the data. • The patterns can suggest "new" facts, relationships, or associations about that person, such as that person's membership in a newly "discovered" category or group.

  33. Data Mining (Continued) • Much personal data collected and used in data-mining applications is generally considered to be neither confidential nor intimate in nature. • So there is a tendency to presume that such data must by default be public data.

  34. Data Mining (Continued) • Hypothetical Scenario (Lee): • Lee is a 35-year old junior executive; • Lee applies for a car loan; • Lee has an impeccable credit history; • A data mining algorithm “discovers” that Lee belongs to a group of individuals likely to start their own business and declare bankruptcy; • Lee is denied the loan based on data mining.

  35. Techniques for Manipulating Personal Data

  36. Data Mining on the Internet • Traditionally, data mining is done in large “data warehouses” (off-line). • "Intelligent agents" or "softbots" acting on behalf of human beings sift through and analyze the mounds of data on the Internet. • Metasearch engines "crawl" through the Web in order to uncover general patterns from information retrieved from search-engine requests across multiple Web sites.

  37. The Problem of Protecting Privacy in Public • Non-Public Personal Information (or NPI) refers to sensitive information such as in one’s financial and medical records. • NPI has some legal protection • Many privacy analysts are now concerned about a different kind of personal information – Public Personal Information (or PPI). • PPI is non-confidential and non-intimate in character – is also being mined.

  38. PPI • Why should the collection of PPI, which is publicly available information about persons generate controversies involving privacy? • it might seem that there is little to worry about. • For example, suppose learns that that you are a student at Rivier, you frequently attend college basketball games, and you are actively involved in Rivier’s computer science club. • In one sense, the information is personal because it is about you (as a person);but it is also about what you do in the public sphere.

  39. PPI (Continued) • In the past, it would have been difficult to make a strong case for such legislation protecting PPI, because lawmakers and ordinary persons would have seen no need to protect that kind of personal information. • Nissenbaum (1997) believes that our earlier assumptions about the need to protect privacy in public are no longer tenable because of a misleading assumption:  • There is a realm of public information about persons to which no privacy norms apply.

  40. PPI (Continued) • Hypothetical Scenario: • (a) Shopping at Supermart; • (b) Shopping at; • Reveal problems of protecting privacy in public in an era of information technology and data mining.

  41. Search Engines and Personal Information • Search facilities can be used to gain personal information about individuals (e.g., the Amy Boyer example). • Your Web activities can be catalogued (Deja News) and referenced by search engines. • Scenario – using a search engine to locate a friend.

  42. Accessing Public Records via the Internet • What are public records? • Why do we have them? • Traditionally, they were accessed via hardcopy documents that resided in municipal buildings. • Recall the Amy Boyer case. • Would it have made a difference?

  43. Accessing Public Records via the Internet (continued) • Some “information merchants” believe that because public records are, by definition, "public," they must be made available online. • They reason: • Public records have always been available to the public. • Public records have always resided in public space. • The Internet is a public space. • Therefore, all of public records ought to be made available on-line.

  44. Accessing Public Records via the Internet (continued) • Two Case illustrations: • State of Oregon (Motor Vehicle Department); • Merrimack, NH (tax records for city residents).

  45. Can Technology Be Used to Protect Personal Privacy? • Privacy advocates have typically argued for stronger privacy laws to protect individuals. • Groups representing the e-commerce sector have lobbied for voluntary controls and industry self-regulation as an alternative to additional privacy legislation. • Now, some members of each camp support a compromise resolution to the on-line privacy debate in the form of privacy-enhancing tools or PETs.

  46. PETs • PETs can be understood as tools that users can employ either to: • (a)protect their personal identity while interacting with the Web; • (b)protect the privacy of communications (such as e-mail) sent over the Internet.

  47. PETs (Continued) • Three Problems with PETs: • (1) Educating Users About the Existence of PETS; • (2) The Principle of Informed Consent; • (3) Issues of Social Equity.

  48. Educating Users About PETs • How are Users supposed to find about PETs? • DeCew (1997) – there should be a presumption in favor of privacy for indiciduals who can then negotiate. • With PETs, the default is that users must discover their existence and learn how to use them.

  49. PETS and the Problem of Informed Consent • Users enter into an agreement with Web site owners (if they have a privacy policy). • They typically have to “opt out” of having information collected. (The default practice is that they have opted in, unlesss they specify otherwise.) • Policies involving PETs can’t guarantee users against secondary and future uses of their information (e.g., the Toysmart case).

  50. PETS and Social Equity • DeCew – principle of “dynamic negotiation.” • Poorer users have fewer options (and some may need to sell their personal information). • Two classes – privacy rich/privacy poor. • Analogy: Poor people in third world countries selling organs for money.