There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

PROBLEM TO BE SOLVED: To solve the problem, wherein it is impossible for an electronic content information provider to provide commercially secure and effective method, for a configurable general-purpose electronic commercial transaction/distribution control system. SOLUTION: In this system, having at least one protected processing environment for safely controlling at least one portion of decoding of digital information, a secure content distribution method comprises a process for encapsulating digital information in one or more digital containers; a process for encrypting at least a portion of digital information; a process for associating at least partially secure control information for managing interactions with encrypted digital information and/or digital container; a process for delivering one or more digital containers to a digital information user; and a process for using a protected processing environment, for safely controlling at least a portion of the decoding of the digital information. COPYRIGHT: (C)2006,JPO&NCIPI

https://apps.dtic.mil/dtic/tr/fulltext/u2/a423264.pdf

Systems and Methods for Secure Transaction Management and Electronic Rights Protection

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain identifying attributes.In this article, we show using two simple attacks that a k-anonymized dataset has some subtle but severe privacy problems. First, an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. This is a known problem. Second, attackers often have background knowledge, and we show that k-anonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks, and we propose a novel and powerful privacy criterion called e-diversity that can defend against such attacks. In addition to building a formal foundation for e-diversity, we show in an experimental evaluation that e-diversity is practical and can be implemented efficiently.

/pdf/l-diversity-privacy-beyond-k-anonymity-54qdj1526d.pdf

L-diversity: Privacy beyond k-anonymity

Peer-to-peer file-sharing networks are currently receiving much attention as a means of sharing and distributing information. However, as recent experience shows, the anonymous, open nature of these networks offers an almost ideal environment for the spread of self-replicating inauthentic files.We describe an algorithm to decrease the number of downloads of inauthentic files in a peer-to-peer file-sharing network that assigns each peer a unique global trust value, based on the peer's history of uploads. We present a distributed and secure method to compute global trust values, based on Power iteration. By having peers use these global trust values to choose the peers from whom they download, the network effectively identifies malicious peers and isolates them from the network.In simulations, this reputation system, called EigenTrust, has been shown to significantly decrease the number of inauthentic files on the network, even under a variety of conditions where malicious peers cooperate in an attempt to deliberately subvert the system.

/pdf/the-eigentrust-algorithm-for-reputation-management-in-p2p-hmpmn26kxv.pdf

The Eigentrust algorithm for reputation management in P2P networks

Today's globally networked society places great demands on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call for the release of specific data (microdata). In order to protect the anonymity of the entities (called respondents) to which information refers, data holders often remove or encrypt explicit identifiers such as names, addresses, and phone numbers. Deidentifying data, however, provides no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly available information to reidentify respondents and inferring information that was not intended for disclosure. In this paper we address the problem of releasing microdata while safeguarding the anonymity of respondents to which the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how k-anonymity can be provided without compromising the integrity (or truthfulness) of the information released by using generalization and suppression techniques. We introduce the concept of minimal generalization that captures the property of the release process not distorting the data more than needed to achieve k-anonymity, and present an algorithm for the computation of such a generalization. We also discuss possible preference policies to choose among different minimal generalizations.

/pdf/protecting-respondents-identities-in-microdata-release-4kv546ufqa.pdf

Protecting respondents identities in microdata release

Access control constrains what a user can do directly, as well as what programs executing on behalf of the users are allowed to do. In this way access control seeks to prevent activity that could lead to a breach of security. This article explains access control and its relationship to other security services such as authentication, auditing, and administration. It then reviews the access matrix model and describes different approaches to implementing the access matrix in practical systems, and follows with a discussion of access control policies commonly found in current systems, and a brief consideration of access control administration. >

Access control: principle and practice

Today's globally networked society places great demand on the dissemination and sharing of person-specific data. Situations where aggregate statistical information was once the reporting norm now rely heavily on the transfer of microscopically detailed transaction and encounter information. This happens at a time when more and more historically public information is also electronically available. When these data are linked together, they provide an electronic shadow of a person or organization that is as identifying and personal as a fingerprint, even when the sources of the information contains no explicit identifiers, such as name and phone number. In order to protect the anonymity of individuals to whom released data refer, data holders often remove or encrypt explicit identifiers such as names, addresses and phone numbers. However, other distinctive data, which we term quasi-identifiers, often combine unquely and can be linked to publicly available information to re-identify individuals.
In this paper we address the problem of releasing person-specific data while, at the same time, safeguarding the anonymity of individuals to whom the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its contents ambiguiously map the information to at least k entities. We illustrate how k-anonymity can be provided by using generalization and suppression techniques. We introduce the concept of minimal generalization, which captures the property of the release process not to distort the data more than needed to achieve k-anonymity. We illustrate possible preference policies to choose among different minimal generalizations. Finally, we present an algorithm and experimental results when an implementation of the algorithm was used to produce releases of real medical information. We also report ont he quality of the released data by measuring precision and completeness of the results for different values of k.

Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression

The proliferation of information on the Internet and access to fast computers with large storage capacities has increased the volume of information collected and disseminated about individuals. The existence os these other data sources makes it much easier to re-identify individuals whose private information is released in data believed to be anonymous. At the same time, increasing demands are made on organizations to release individualized data rather than aggregate statistical information. Even when explicit identi ers, such as name and phone number, are removed or encrypted when releasing individualized data, other characteristic data, which we term quasi-identi ers, can exist which allow the data recipient to re-identify individuals to whom the data refer. In this paper, we provide a computational disclosure technique for releasing information from a private table such that the identity of any individual to whom the released data refer cannot be de nitively recognized. Our approach protects against linking to other data. It is based on the concepts of generalization, by which stored values can be replaced with semantically consistent and truthful but less precise alternatives, and of k-anonymity . A table is said to provide k-anonymity when the contained data do not allow the recipient to associate the released information to a set of individuals smaller than k. We introduce the notions of generalized table and of minimal generalization of a table with respect to a k-anonymity requirement. As an optimization problem, the objective is to minimally distort the data while providing adequate protection. We describe an algorithm that, given a table, e ciently computes a preferred minimal generalization to provide anonymity.

Generalizing data to provide anonymity when disclosing information (abstract)

Although several access control policies can be devised for controlling access to information, all existing authorization models, and the corresponding enforcement mechanisms, are based on a specific policy (usually the closed policy). As a consequence, although different policy choices are possible in theory, in practice only a specific policy can actually be applied within a given system. In this paper, we present a unified framework that can enforce multiple access control policies within a single system. The framework is based on a language through which users can specify security policies to be enforced on specific accesses. The language allows the specification of both positive and negative authorizations and incorporates notions of authorization derivation, conflict resolution, and decision strategies. Different strategies may be applied to different users, groups, objects, or roles, based on the needs of the security policy. The overall result is a flexible and powerful, yet simple, framework that can easily capture many of the traditional access control policies as well as protection requirements that exist in real-world applications, but are seldom supported by existing systems. The major advantage of our approach is that it can be used to specify different access control policies that can all coexist in the same system and be enforced by the same security server.

/pdf/flexible-support-for-multiple-access-control-policies-26x5p9wudp.pdf

Pierangela Samarati

Papers

Protecting respondents identities in microdata release

Access control: principle and practice

Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression

Generalizing data to provide anonymity when disclosing information (abstract)

Flexible support for multiple access control policies