Crime statistics facilitate benchmarking and analysis of crime trends. Crime analysts use criminal statistics to spot emerging trends and unique modi operandi. Patrol officers and detectives use this data to prevent future crimes and to apprehend offenders.
In many police departments, detectives often compile and report crime data. Thus, homicide detectives count the number of murders, sexual assault investigators examine the number of rapes, and auto detectives count car thefts. Computer crime, on the other hand, comprises such an ill-defined list of offenses that various units within a police department usually keep the related data separately, if they keep them at all. For example, the child abuse unit likely would maintain child pornography arrest data and identify the crime as the sexual exploitation of a minor. A police department’s economic crimes unit might recap an Internet fraud scam as a simple fraud, and an agency’s assault unit might count an on-line stalking case as a criminal threat. Because most police organizations do not have a cohesive entity that measures offenses where criminals either criminally target a computer or use one to perpetrate a crime, accurate statistics remain difficult to obtain.
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Crime analysts use criminal statistics to spot emerging trends and unique modi operandi. Patrol officers and detectives use this data to prevent future crimes and to apprehend offenders. Therefore, to count computer crime, a general agreement on what constitutes a computer crime must exist.
In many police departments, detectives often compile and report crime data. Thus, homicide detectives count the number of murders, sexual assault investigators examine the number of rapes, and auto detectives count car thefts. Computer crime, on the other hand, comprises such an ill-defined list of offenses that various units within a police department usually keep the related data separately, if they keep them at all. For example, the child abuse unit likely would maintain child pornography arrest data and identify the crime as the sexual exploitation of a minor. A police department’s economic crimes unit might recap an Internet fraud scam as a simple fraud, and an agency’s assault unit might count an on-line stalking case as a criminal threat. Because most police organizations do not have a cohesive entity that measures offenses where criminals either criminally target a computer or use one to perpetrate a crime, accurate statistics remain difficult to obtain.
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The privacy issue lies at the heart of an ongoing debate in nearly all Western democracies between liberalists and communitarians over the question how to balance individual rights and collective goods. The privacy issue is concerned more specifically with the question how to balance the claims of those who want to limit the availability of personal information in order to protect individuals and the claims of those who want to make information about individuals available in order to benefit the community. This essential tension emerges in many privacy discussions, e.g. undercover actions by the police on the internet, use of Closed Circuit Television in public places, making medical files available for health insurance purposes or epidemiological research, linking and matching of databases to detect fraud in social security, soliciting information about on-line behavior of internet users from access providers in criminal justice cases. Communitarians typically argue that the community benefits significantly from having knowledge about its members available. According to communitarians modern Western democracies are in a deplorable condition and our unquenchable thirst for privacy serves as its epitome. Who could object to having his or her data accessed if honorable community causes are served? Communitarians also point out that modern societies exhibit high degrees of mobility, complexity and anonymity. As they are quick to point out, crime, free riding, and the erosion of trust are rampant under these conditions. Political philosopher Michael Walzer observes that “Liberalism is plagued by free-rider problems, by people who continue to enjoy the benefits of membership and identity while no longer participating in the activities that produce these benefits. Communitarianism, by contrast, is the dream of a perfect free-riderlessness”. The modern Nation States with their complex public administrations need a steady input of personal information to function well or to function at all. In post-industrial societies ‘participation in producing the benefits’ often takes the form of making information about one-self available. Those who are responsible for managing the public goods therefore insist on removing constraints on access to personal information and tend to relativize the importance of privacy of the individual.
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At the foundation of this view is a conception of the employment relationship as involving a voluntary exchange of property. The employer agrees to exchange property in the form of a wage or salary for the employee’s labor. Conceived as a free exchange, the employment relationship, in the absence of some express contractual duration requirement, can be terminated at will by either party for nearly any reason. Exceptions to the employment-at-will doctrine include firing someone for serving on jury duty, for reporting violations of certain federal regulations, or for impermissible race, sex, or age discrimination on the employer’s part. Accordingly, the terms and conditions of employment are largely up to the parties to decide.
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According to Kurt Thearling (1995), Ph.D. a senior director of Wheelhouse Corporation, “data mining” is a set of automated techniques used to extract or previously unknown pieces of information from large databases. He points out that data mining is not a business solution but simply the underlying technology. In technical terms, data mining is described as the application of artificial intelligence (AI) and other intelligent techniques such as neural networks, fuzzy logic, genetic algorithms, decision trees, nearest neighbor method, rule induction, and data visualization, to large quantities of data to discover hidden trends, patterns, and relationships. Cavoukian (1998), Ph.D, the Information and Privacy Commissioner of Ontario, says that successful data mining makes it possible to reveal patterns and relationships, and then use this “new” information to make proactive knowledge-driven business decisions.
Data mining is often confused with other terms such as Knowledge Discovery in Database (KDD) or On-Line Analytical Processing (OLAP) (Tavani, 1999; Mena, 1999). First, KDD is distinguished from data mining because KDD process includes the work done before the data is searched for patterns, as well as the work done on the patterns after searching which uses deductive reasoning. “Whereas KDD is the overall process of discovering useful knowledge from data, data mining is a particular step in that process” (Tavani, 1999: 265). Secondly, differing from OLAP which uses deductive reasoning, data mining uses inductive reasoning. Thus data mining does not rely on the user to determining information from data, which, in other words, data mining does not require users to directly query the database.
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Personal data is commonly defined as data and information relating to an identified or identifiable person. A clear illustration of this rather narrow starting point can be found in the highly influential European Directive 95/46/EC of the European Parliament and of the European Council of 24 October 1995, “on the protection of individuals with regard to the processing of personal data and on the free movement of such data.” Because a European Directive must be implemented in the national law and regulation of European Union countries, the definitions and principles formulated in the Directive are mirrored in the national privacy laws and regulations throughout the European Union. With regard to the processing of personal data, the Directive poses some basic principles. For the purposes of this paper, I will highlight some of these. It is important to notice that—as may be expected from the definition of personal data—most of these principles lean heavily on the idea that there is some kind of direct connection between a designate person and his or her data.
There are some principles regarding data quality. Personal data should only be collected for specified, explicit, legitimate purposes and should not be further processed in a way incompatible with these purposes. No excessive amounts of data should be collected, relative to the purpose for which the data is collected. Moreover, the data should be accurate and, if applicable, kept up to date. Every reasonable step must be taken to ensure that inaccurate or incomplete data is either rectified or erased. Also, personal data should be kept in a form that permits identification of data subjects for no longer than is necessary for the purpose for which the data were collected.
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Many influential approaches to privacy emphasize the role of privacy in safeguarding a personal or intimate realm where people may escape the prying and interference of others. This private realm, which is contrasted with a public realm, is defined in various ways. It is delimited by physical boundaries, such as the home; by personal relationships, such as family, friends, and intimates; and by selected fields of information, such as personal, sensitive, or embarrassing information.
Privacy is worthy of safeguarding, these approaches argue, because intimacy is important; privacy is worth protecting because we value the sanctity of a personal realm. This article does not dispute the importance of securing intimate and personal realms. Nor does it challenge the compelling connection between privacy norms and the ability to protect these realms against unwarranted intrusion. It argues, however, that an account of privacy is not complete that stops with the intimate and personal realms. The widespread use of information technology, such as in personal profiling, to assemble and transmit vast stores of information–even so-called “public” information-has shown than an adequate account of privacy should neither neglect the non-intimate realm nor explicitly exclude it from consideration.
Loud calls of public protest in response to information harvesting strongly indicate that implicit norms of privacy are not restricted to personal zones. I henceforth call this challenge to existing theoretical frameworks the problem of protecting “privacy in public.”
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According to one well known argument there is no right to privacy and there is nothing special about privacy, because any interest protected as private can be equally well explained and protected by other interests or rights, most notably rights to property and bodily security (Thomson, 1975). Other critiques argue that privacy interests are not distinctive because the personal interests they protect are economically inefficient (Posner, 1981) or that they are not grounded in any adequate legal doctrine (Bork, 1990). Finally, there is the feminist critique of privacy, that granting special status to privacy is detrimental to women and others because it is used as a shield to dominate and control them, silence them, and cover up abuse (MacKinnon, 1989).
Other commentators defend privacy as necessary for the development of varied and meaningful interpersonal relationships (Fried, 1970, Rachels, 1975), or as the value that accords us the ability to control the access others have to us (Gavison, 1980; Allen, 1988; Moore, 2003), or as a set of norms necessary not only to control access but also to enhance personal expression and choice (Schoeman, 1992), or some combination of these (DeCew, 1997). Discussion of the concept is complicated by the fact that privacy appears to be something we value to provide a sphere within which we can be free from interference by others, and yet it also appears to function negatively, as the cloak under which one can hide domination, degradation, or physical harm to women and others.
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It defined personal data as any information relating to an identified or identifiable natural person, and processing of personal data – as any operation or set of operations which is performed upon personal data (and enumerated these operations). It introduced a catalogue of minimum rights for persons whose data are collected. The violation of these rights would result in a possibility to pursue these rights before court. Admissibility of data processing was made dependent on the data subject’s will (consent). However, a closed catalogue of situations in which data processing is possible without such consent was specified. The Directive determines a group of so called sensitive data. In case of their processing a written consent is required. Also data relating to criminal convictions, which can be processed only by public entities, were handled separately in the Directive. Possible exemptions from the principle of ban on the processing of such data were specified. At the same time, pursuant to the Directive, data can be used exclusively for the purpose for which they were collected. The Directive introduced an obligation to inform persons about the principles of their data processing before the collection of these data. The person concerned can object to the processing of his/her data, provided that he or she has a legitimate purpose. Any person whose data were included in the filing system has the right to ask about the principles of data processing, starting with a possibility to obtain information on the controller, and ending with indication of the contents of these data. The Directive introduced as well the right for the data subject to control his/her data, including the right to object to the processing of data. Pursuant to the Directive, any person who has suffered damage as a result of an unlawful data processing incompatible with the Directive is entitled to receive compensation. One of the most important regulations introduced by the Directive is the issue of personal data transfer to third countries (such transfer is possible in case where the third country ensures an adequate level of protection).
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After surveying circumstances and activities that give rise to the problem of privacy in public, I offer an explanation for why predominant and influential theoretical accounts of privacy have failed to deal explicitly with it. Following this, in what may be seen as the core of the paper, I identify the features of contemporary surveillance practices that are central to viewing these practices as genuine concerns for any normative theory of privacy. In the concluding sections of the paper, I consider how we may absorb privacy in public into comprehensive theories of privacy.
I also clear the way for such a theory by showing how certain barriers that, in the past, have seemed insurmountable may be overcome.
Before responding directly to the challenge of producing principles by which Lotus Marketplace Households and similar efforts may be judged violations of privacy, I consider the reasons why many influential philosophical theories of privacy may not have addressed directly the cluster of issues raised by widespread public surveillance. If privacy in public does constitute a genuine privacy interest, then not only is it important to construct the much needed justificatory framework, but also to ask why philosophical and normative theories of privacy have either explicitly dismissed the idea of any genuine privacy interest in public, or merely have overlooked it.
A variety of factors have shaped normative theories of privacy, making them more responsive to some types of problems and constraints and less responsive to others. Examining these theories with a view to understanding why specifically they either neglect or dismiss the normative force of privacy in public, three factors (there may be others) emerge, which I have labeled, respectively, conceptual, normative, and empirical.
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