Privacy Threshold Research Articles

K-Nearest neighbor query processing algorithm for cloaking regions towards user privacy protection in location-based services

Due to the advancement of wireless internet and mobile positioning technology, the application of location-based services (LBSs) has become popular for mobile users. Since users have to send their exact locations to obtain the service, it may lead to several privacy threats. To solve this problem, a cloaking method has been proposed to blur users' exact locations into a cloaked spatial region with a required privacy threshold (k). With the cloaked region, an LBS server can carry out a k-nearest neighbor (k-NN) search algorithm. Some recent studies have proposed methods to search k-nearest POIs while protecting a user's privacy. However, they have at least one major problem, such as inefficiency on query processing or low precision of retrieved result. To resolve these problems, in this paper, we propose a novel k-NN query processing algorithm for a cloaking region to satisfy both requirements of fast query processing time and high precision of the retrieved result. To achieve fast query processing time, we propose a new pruning technique based on a 2D-coodinate scheme. In addition, we make use of a Voronoi diagram for retrieving the nearest POIs efficiently. To satisfy the requirement of high precision of the retrieved result, we guarantee that our k-NN query processing algorithm always contains the exact set of k nearest neighbors. Our performance analysis shows that our algorithm achieves better performance in terms of query processing time and the number of candidate POIs compared with other algorithms.

Facebook and User-Controlled Privacy: Evaluating Privacy Settings as Notice-and-Consent

“Notice and consent” is a widely-used model of privacy protection that relies on users’ ability to interpret an organization’s written description of its information practices through a privacy policy and choose whether to participate. In the past, organizations using this model have fallen under harsh criticism for providing long, legalistic notices and confusing, unclear choices. In the past few years, Facebook has sought to address these issues through privacy settings such as the “Privacy Publisher tool.” These mechanisms are new and untested, but they could potentially vindicate notice-and-consent by using graphics, metrics and other mechanisms to better inform and facilitate privacy decisions. The study will test the efficacy of these mechanisms for providing appropriate protection. In doing so, it will evaluate their feasibility as a model for notice and consent. To conduct this evaluation, the thesis will conduct a behavioral survey of how site visitors use these settings to understand their privacy risk and make decisions based on personal preferences. The study hypothesizes that, while privacy settings may provide some protection, they leave most users unprotected from many significant privacy harms. The survey itself will consist of three sections. The first section will test respondents’ ability to use the privacy settings to protect against specific harms. The survey will present the respondents with the mock privacy pages and prompt them to choose settings in response to a privacy issue. One prompt may be: “Choose Facebook settings that would only allow friends to tag photos of you.” The results could indicate whether the settings discourage certain kinds of protections, such as content sharing restrictions on Facebook. The second section will examine how respondents currently apply their privacy preferences on Facebook. Respondents will begin by submitting their Facebook privacy settings via multiple choice answers. Respondents will then answer questions about their preferences for each privacy practice. This section will permit the study to compare users’ opinion on the issues with how they actually selected their settings and will be an important indicator for evaluating the privacy settings. Finally, the third section will collect information of demographics and Facebook usage, such as how often a user checks Facebook for updates or posts to his or her wall. The final report will include a statistical analysis of these results and an examination of the conclusion. The study will conduct an analysis of variance, for instance, to understand how significant the gaps were between respondents’ selected settings and the settings that actually protect their preferences. The study will also employ regression techniques to understand which privacy settings face similar usability obstacles and which demographics face greater risks to privacy. David Krone is a recent graduate of Georgetown University’s Communications, Culture and Technology Program. David conducted research on Facebook Privacy as part of his thesis, with Georgetown Professors Mark MacCarthy and Diana Owen as advisors. In addition, David works full time as a Cybersecurity and Privacy Analyst at the Office of Personnel Management where he holds several privacy-related responsibilities. These include assessing IT system security risks to conducting system privacy threshold analyses (PTAs) and compiling reports on breaches involving personally identifiable information (PII). David holds the certification for the CIPP/G (Certified Information Privacy Professional – Government). Finally, as a Georgetown undergraduate, David studied the intersection of society and information and communications technologies by majoring in Science, Technology and International Affairs and minoring in Computer Science.

Secure quantum private information retrieval using phase-encoded queries

We propose a quantum solution to the classical private information retrieval (PIR) problem, which allows one to query a database in a private manner. The protocol offers privacy thresholds and allows the user to obtain information from a database in a way that offers the potential adversary, in this model the database owner, no possibility of deterministically establishing the query contents. This protocol may also be viewed as a solution to the symmetrically private information retrieval problem in that it can offer database security (inability for a querying user to steal its contents). Compared to classical solutions, the protocol offers substantial improvement in terms of communication complexity. In comparison with the recent quantum private queries [Phys. Rev. Lett. 100, 230502 (2008)] protocol, it is more efficient in terms of communication complexity and the number of rounds, while offering a clear privacy parameter. We discuss the security of the protocol and analyze its strengths and conclude that using this technique makes it challenging to obtain the unconditional (in the information-theoretic sense) privacy degree; nevertheless, in addition to being simple, the protocol still offers a privacy level. The oracle used in the protocol is inspired both by the classical computational PIR solutionsmore » as well as the Deutsch-Jozsa oracle.« less

Secure, pseudonymous, and auditable communication in vehicular ad hoc networks

AbstractVehicular ad hoc networks (VANETs) represent a promising new communication technology that can facilitate many new forms of automotive applications. Many of the applications that will run on these networks will require a high degree of security and privacy. In this paper, we present a robust and efficient security framework for vehicular networks suited for both inter‐vehicular and vehicle‐to‐infrastructure communication. Our system uses identity‐based cryptography (IBC) to provide authentication, confidentiality, non‐repudiation, and message integrity. It exploits the implicit authentication provided by IBC to significantly reduce the security/privacy‐related communication overhead. Additionally, it provides scalable, user‐customizable security and privacy using short‐lived, authenticated, and unforgeable, pseudonyms. This feature can be used by VANET applications that require quantifiable trust and privacy to provide differentiated service based on various levels of trust and privacy thresholds. Copyright © 2008 John Wiley & Sons, Ltd.

Association rule hiding

Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that one may encounter when releasing data to outside parties. A key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. Every disclosure limitation method affects, in some way, and modifies true data values and relationships. We investigate confidentiality issues of a broad category of rules, the association rules. In particular, we present three strategies and five algorithms for hiding a group of association rules, which is characterized as sensitive. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public since, among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. We also perform an evaluation study of the hiding algorithms in order to analyze their time complexity and the impact that they have in the original database.

Relationship marketing and privacy: exploring the thresholds

The advocacy to extend relationship marketing into the management of the exchange processes within retailing and consumer service markets has meant a growing interest in the opportunities which are offered by technology‐based systems of customer management. A prerequisite for the successful translation of the relationship marketing paradigm from organizational to retailing and consumer service markets is customer information. As the technology for collecting customer data has become more sophisticated, so marketing managers have witnessed an increasing concern among consumers about the impact of these new marketing management techniques on their private rights. Discusses different aspects of information privacy and reports an exploratory study of consumers’ different privacy thresholds. The results indicate different levels of involvement towards privacy issues, and different perceptions of the trustworthiness of service sector companies, among consumers. The implications for relationship marketing strategies are discussed.