Privacy Preserving Data Publishing for Recommender System

Chen, Xiaoqiang; Huang, Vincent

doi:10.1109/compsacw.2012.33

Cited by 16 publications

(13 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many privacy definitions and schemes for releasing data have been proposed in the past (see [1] and [2] for surveys). However, many of them have been shown to be insufficient due to realistic attacks on such schemes (e.g., see [3]).…”

Section: Introductionmentioning

confidence: 99%

Crowd-Blending Privacy

Gehrke

Hay

Lui

et al. 2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract.We introduce a new definition of privacy called crowdblending privacy that strictly relaxes the notion of differential privacy. Roughly speaking, k-crowd blending private sanitization of a database requires that each individual i in the database "blends" with k other individuals j in the database, in the sense that the output of the sanitizer is "indistinguishable" if i's data is replaced by j's.We demonstrate crowd-blending private mechanisms for histograms and for releasing synthetic data points, achieving strictly better utility than what is possible using differentially private mechanisms. Additionally, we demonstrate that if a crowd-blending private mechanism is combined with a "pre-sampling" step, where the individuals in the database are randomly drawn from some underlying population (as is often the case during data collection), then the combined mechanism satisfies not only differential privacy, but also the stronger notion of zero-knowledge privacy. This holds even if the pre-sampling is slightly biased and an adversary knows whether certain individuals were sampled or not. Taken together, our results yield a practical approach for collecting and privately releasing data while ensuring higher utility than previous approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

Crowd-Blending Privacy

Gehrke

Hay

Lui

et al. 2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Sampling is commonly used in real-world PPDP [19]. However, its effect on hiding presence is not clear.…”

Section: Related Workmentioning

confidence: 99%

“…The second metric is information gain (KullbackLeibler divergence), which is commonly used in the statistics community [19] and is one of the most general metrics that react to changes in correlation among attributes, in contrast with NCP. The information gain D KL of T from T is defined as follows:…”

Section: Information Loss Metricsmentioning

confidence: 99%

<i>k</i>-Presence-Secrecy: Practical Privacy Model as Extension of <i>k</i>-Anonymity

Yamaoka

Itoh

2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYPPDP (Privacy-Preserving Data Publishing) is technology that discloses personal information while protecting individual privacy. kanonymity is a privacy model that should be achieved in PPDP. However, k-anonymity does not guarantee privacy against adversaries who have knowledge of even a few uncommon individuals in a population. In this paper, we propose a new model, called k-presence-secrecy, that prevents such adversaries from inferring whether an arbitrary individual is included in a personal data table. We also propose an algorithm that satisfies the model. k-presence-secrecy is a practical model because an algorithm that satisfies it requires only a PPDP target table as personal information, whereas previous models require a PPDP target table and almost all the background knowledge of adversaries. Our experiments show that, whereas an algorithm satisfying only k-anonymity cannot protect privacy, even against adversaries who have knowledge for one uncommon individual in a population, our algorithm can do so with less information loss and shorter execution time.

show abstract

“…These approaches are intended to infer the probabilities of linking personal data attributes to each other and to the principal they describe. For instance, there are approaches that deal with the inference problem when querying databases (Cuenca Grau and Horrocks, 2008), when applying data mining techniques (Zhu et al, 2009), in social networks (Zheleva and Getoor, 2009), and in general, in all activities that require the publication of data (Chen et al, 2009). All of these approaches consider complex models of personal information inference.…”

Section: Personal Data Attribute Inferencementioning

confidence: 99%

A survey of privacy in multi-agent systems

Such

Espinosa

García-Fornes

2013

The Knowledge Engineering Review

View full text Add to dashboard Cite

Privacy has been a concern for humans long before the explosive growth of the Internet. The advances in information technologies have further increased these concerns. This is because the increasing power and sophistication of computer applications offers both tremendous opportunities for individuals, but also significant threats to personal privacy. Autonomous agents and Multi-agent Systems are examples of the level of sophistication of computer applications. Autonomous agents usually encapsulate personal information describing their principals, and therefore they play a crucial role in preserving privacy. Moreover, autonomous agents themselves can be used to increase the privacy of computer applications by taking advantage of the intrinsic features they provide, such as artificial intelligence, pro-activeness, autonomy, and the like. This article introduces the problem of preserving privacy in computer applications and its relation to autonomous agents and Multi-agent Systems. It also surveys privacy-related studies in the field of Multi-agent Systems and identifies open challenges to be addressed by future research.

show abstract

Privacy Preserving Data Publishing for Recommender System

Cited by 16 publications

References 48 publications

Crowd-Blending Privacy

Crowd-Blending Privacy

<i>k</i>-Presence-Secrecy: Practical Privacy Model as Extension of <i>k</i>-Anonymity

A survey of privacy in multi-agent systems

Contact Info

Product

Resources

About