Lex Informatica: The Formulation of Information Policy Rules through Technology

Historically, law and government regulation have established default rules for information policy, including constitutional rules on freedom of expression and statutory rights of ownership of information. This Article will show that for network environments and the Information Society, however, law and government regulation are not the only source of rule-making. Technological capabilities and system design choices impose rules on participants. The creation and implementation of information policy are embedded in network designs and standards as well as in system configurations. Even user preferences and technical choices create overarching, local default rules. This Article argues, in essence, that the set of rules for information flows imposed by technology and communication networks form a “Lex Informatica” that policymakers must understand, consciously recognize, and encourage

A Utility-Theoretic Approach to Privacy in Online Services

Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users’ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples’ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users

Profiling user activities with minimal traffic traces

Understanding user behavior is essential to personalize and enrich a user's online experience. While there are significant benefits to be accrued from the pursuit of personalized services based on a fine-grained behavioral analysis, care must be taken to address user privacy concerns. In this paper, we consider the use of web traces with truncated URLs - each URL is trimmed to only contain the web domain - for this purpose. While such truncation removes the fine-grained sensitive information, it also strips the data of many features that are crucial to the profiling of user activity. We show how to overcome the severe handicap of lack of crucial features for the purpose of filtering out the URLs representing a user activity from the noisy network traffic trace (including advertisement, spam, analytics, webscripts) with high accuracy. This activity profiling with truncated URLs enables the network operators to provide personalized services while mitigating privacy concerns by storing and sharing only truncated traffic traces. In order to offset the accuracy loss due to truncation, our statistical methodology leverages specialized features extracted from a group of consecutive URLs that represent a micro user action like web click, chat reply, etc., which we call bursts. These bursts, in turn, are detected by a novel algorithm which is based on our observed characteristics of the inter-arrival time of HTTP records. We present an extensive experimental evaluation on a real dataset of mobile web traces, consisting of more than 130 million records, representing the browsing activities of 10,000 users over a period of 30 days. Our results show that the proposed methodology achieves around 90% accuracy in segregating URLs representing user activities from non-representative URLs

An Examination of Privacy Policies of Global University Web Sites

Due to demand in online services, universities throughout the world are increasing the content of their Web sites and adding features, such as online applications and e-learning. However, adding online services requires that personal data is kept within computerized systems, thus putting personal private information at risk. Online consumers express concern about the risk of their personal private data and demand to know how organizations will protect their records. It is imperative that firms have mechanisms to guard their data and publish protection information within online privacy policies to mitigate user distrust. However, although industry privacy groups may recommend better protection and some countries may legislate its use; this is not universal in all university sites. This study analyzes 90 universities site throughout the world to determine the use of privacy protection. The results show a lack of use of certain privacy mechanisms. The research suggests methods for improving protection

A Privacy Protection in Personalized Web Search for Knowledge Mining: A Survey

The web search engines (e.g. Google, Yahoo etc.) help the users to find required useful information on the World Wide Web (WWW). But it has become increasingly difficult to get the expected results from the web search engine because contentsare available in web is very vast and ambiguous.Due to tremendous data opportunities in the internet, the privacy protection is very essential to preserve user search behaviors and their profiles. In this paper system present a novel protocol specially designed to protect the users’ privacy in front of web search profiling. Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. Our runtime generalization aims at striking a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the generalized profile. System proposed two greedy algorithms namely GreedyDP and GreedyIL. These two algorithms are used for runtime generalization.The proposed protocol preserves the privacy of the individuals who deal with a web search engine.System provides a distorted user profile to the web search engine. It offers implementation details and computational and communication results that show that the proposed protocol improves the existing solutions in terms of query delay