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Information privacy

About: Information privacy is a(n) research topic. Over the lifetime, 25412 publication(s) have been published within this topic receiving 579611 citation(s). The topic is also known as: data privacy & data protection.

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Papers
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Journal ArticleDOI: 10.1142/S0218488502001648
Latanya Sweeney1Institutions (1)
Abstract: Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, µ-Argus and k-Similar provide guarantees of privacy protection.

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Topics: k-anonymity (67%), Data anonymization (58%), Information privacy (58%) ...read more

7,135 Citations


Open accessBook ChapterDOI: 10.1007/11681878_14
04 Mar 2006-
Abstract: We continue a line of research initiated in [10,11]on privacy-preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f = ∑ig(xi), where xi denotes the ith row of the database and g maps database rows to [0,1]. We extend the study to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f. Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive.

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Topics: Noise (53%), Differential privacy (52%), Information privacy (52%) ...read more

4,537 Citations


Open accessJournal ArticleDOI: 10.1109/TCSVT.2003.818349
Anil K. Jain1, Arun Ross2, Salil PrabhakarInstitutions (2)
Abstract: A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.

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  • Fig. 10. An improvement in matching accuracy is obtained when face recognition and fingerprint recognition systems are combined in an identification system developed by Hong and Jain [13].
    Fig. 10. An improvement in matching accuracy is obtained when face recognition and fingerprint recognition systems are combined in an identification system developed by Hong and Jain [13].
  • Fig. 1. Block diagrams of enrollment, verification, and identification tasks are shown using the four main modules of a biometric system, i.e., sensor, feature extraction, matcher, and system database.
    Fig. 1. Block diagrams of enrollment, verification, and identification tasks are shown using the four main modules of a biometric system, i.e., sensor, feature extraction, matcher, and system database.
  • Fig. 9. Various scenarios in a multimodal biometric system.
    Fig. 9. Various scenarios in a multimodal biometric system.
  • Fig. 8. Different levels of fusion in a parallel fusion mode: (a) fusion at the feature extraction level, and (b) fusion at matching score (confidence or rank) level, and (c) fusion at decision (abstract label) level. In all the three cases, the final class label is “Accept” or “Reject” when the biometric system is operating in the verification mode or the identity of the best matched user when operating in the identification mode. In (c), the intermediate abstract label(s) could be “Accept” or “Reject” in a verification system or a subset of database users in an identification system.
    Fig. 8. Different levels of fusion in a parallel fusion mode: (a) fusion at the feature extraction level, and (b) fusion at matching score (confidence or rank) level, and (c) fusion at decision (abstract label) level. In all the three cases, the final class label is “Accept” or “Reject” when the biometric system is operating in the verification mode or the identity of the best matched user when operating in the identification mode. In (c), the intermediate abstract label(s) could be “Accept” or “Reject” in a verification system or a subset of database users in an identification system.
  • TABLE I COMPARISON OF VARIOUS BIOMETRIC TECHNOLOGIES BASED ON THE PERCEPTION OF THE AUTHORS. HIGH, MEDIUM, AND LOW ARE DENOTED BY H, M, AND L, RESPECTIVELY
    TABLE I COMPARISON OF VARIOUS BIOMETRIC TECHNOLOGIES BASED ON THE PERCEPTION OF THE AUTHORS. HIGH, MEDIUM, AND LOW ARE DENOTED BY H, M, AND L, RESPECTIVELY
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Topics: Biometrics (56%), Speaker recognition (53%), Password (52%) ...read more

4,384 Citations


Open accessJournal Article
Abstract: We continue a line of research initiated in [10, 11] on privacy-preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f = Σ i g(x i ), where x i denotes the ith row of the database and g maps database rows to [0,1]. We extend the study to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f. Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive.

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Topics: Noise (53%), Function (mathematics) (51%), Information privacy (51%)

3,629 Citations


Journal ArticleDOI: 10.1145/1217299.1217302
Abstract: 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.

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Topics: Information privacy (66%), Privacy software (62%), t-closeness (59%) ...read more

3,549 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202228
20211,523
20201,628
20191,253
20181,277
20171,730

Top Attributes

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Topic's top 5 most impactful authors

Elisa Bertino

80 papers, 3.6K citations

Graham Greenleaf

72 papers, 484 citations

Rongxing Lu

54 papers, 3.4K citations

Lorrie Faith Cranor

37 papers, 3.7K citations

Heng Xu

36 papers, 3.7K citations

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