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2nd International Workshop on Citizen Sensor Networks (CitiSens)

作者： Nin, Jordi Univ Politecn Catalunya BarcelonaTech BSC Barcelona Catalonia Spain

ISBN: (纸本)9783319041780;9783319041773

Microaggregation is one of the most commonly employed microdata protection methods. The basic idea of microaggregation is to anonymize data by aggregating original records into small groups of at least k elements and, therefore, preserving k-anonymity. Usually, in order to avoid information loss, when records are large, i.e., the number of attributes of the data set is large, this data set is split into smaller blocks of attributes and microaggregation is applied to each block, successively and independently. This is called multivariate microaggregation. By using this technique, the information loss after collapsing several values to the centroid of their group is reduced. Unfortunately, with multivariate microaggregation, the k-anonymity property is lost when at least two attributes of different blocks are known by the intruder, which might be the usual case. In this work, we present a new microaggregation method called one dimension microaggregation (Mic1D - k). With Mic1D - k, the problem of k-anonymity loss is mitigated by mixing all the values in the original microdata file into a single non-attributed data set using a set of simple pre-processing steps and then, microaggregating all the mixed values together. Our experiments show that, using real data, our proposal obtains lower disclosure risk than previous approaches whereas the information loss is preserved.

关键词： Microaggregation k-anonymity privacy in statistical databases

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On the use of economic price theory to find the optimum levels of privacy and information utility in non-perturbative microdata anonymisation

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DATA & KNOWLEDGE ENGINEERING 2010年第5期69卷 399-423页

作者： Zielinski, Marek P. Olivier, Martin S. Univ Pretoria SAP Reasearch ZA-0020 Pretoria South Africa

Ideally, when microdata is anonymised, we should maximize both the level of privacy and the level information utility of a released microdata set. However, since privacy and information utility are conflicting requirements, it is difficult to find a good balance between the two goals. The objective and constraints of this optimization problem can be captured naturally with concepts from economic price theory. In this paper, we present an approach based on economic price theory for guiding the process of microdata anonymisation such that optimum levels of privacy and information utility are achieved. (C) 2009 Elsevier B.V. All rights reserved.

关键词： privacy in statistical databases Data quality privacy Information utility Economic price theory

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User-private information retrieval based on a peer-to-peer community

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DATA & KNOWLEDGE ENGINEERING 2009年第11期68卷 1237-1252页

作者： Domingo-Ferrer, Josep Bras-Amoros, Maria Wu, Qianhong Manjon, Jesus Univ Rovira & Virgili Dept Comp Engn & Math UNESCO Chair Data Privacy E-43007 Tarragona Catalonia Spain

Private information retrieval (PIR) is normally modeled as a game between two players: a user and a database. The user wants to retrieve some item from the database without the latter learning which item is retrieved. Most current PIR protocols are ill-suited to provide PIR from a search engine or large database: (i) their computational complexity is linear in the size of the database;(ii) they assume active cooperation by the database server in the PIR protocol. If the database cannot be assumed to cooperate, a peer-to-peer (P2P) user community is a natural alternative to achieve some query anonymity: a user gets her queries submitted on her behalf by other users in the P2P community. In this way, the database still learns which item is being retrieved, but it cannot obtain the real query histories of users, which become diffused among the peer users. We name this relaxation of PIR user-private information retrieval (UPIR). A peer-to-peer UPIR system is described in this paper which relies on an underlying combinatorial structure to reduce the required key material and increase availability. Extensive simulation results are reported and a distributed key management version of the system is described. (C) 2009 Elsevier B.V. All rights reserved.

关键词： privacy in statistical databases Private information retrieval Combinatorial designs Ring signatures

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Towards the evaluation of time series protection methods

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INFORMATION SCIENCES 2009年第11期179卷 1663-1677页

作者： Nin, Jordi Torra, Vicenc CSIC Artificial Intelligence Res Inst IIIA Bellaterra 08193 Catalonia Spain

The goal of statistical disclosure control (SDC) is to modify statistical data so I hat it can be published without releasing confidential information that may be linked to specific respondents. The challenge for SDC is to achieve this variation with minimum loss of the detail and accuracy sought by final users. There are many approaches to evaluate the quality of a protection method. However, all these measures are only applicable to numerical or categorical attributes. In this paper, we present some recent results about time series protection and re-identification. We propose a complete framework to evaluate time series protection methods. We also present some empirical results to show how our framework works. (C) 2009 Elsevier Inc. All rights reserved.

关键词： statistical disclosure control Information loss measures Time series privacy privacy in statistical databases

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On the disclosure risk of multivariate microaggregation

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DATA & KNOWLEDGE ENGINEERING 2008年第3期67卷 399-412页

作者： Nin, Jordi Herranz, Javier Torra, Vicenc Spanish Natl Res Council CSIC Artificial Intelligence Res Inst IIIA Bellaterra 08193 Catalonia Spain

The aim of data protection methods is to protect a microdata file both minimizing the disclosure risk and preserving the data utility. Microaggregation is one of the most popular such methods among statistical agencies. Record linkage is the standard mechanism used to measure the disclosure risk of a microdata protection method. However, only standard, and quite generic, record linkage methods are usually considered, whereas more specific record linkage techniques can be more appropriate to evaluate the disclosure risk of some protection methods. In this paper we present a new record linkage technique, specific for microaggregation, which obtains more correct links than standard techniques. We have tested the new technique with MDAV microaggregation and two other microaggregation methods, based on projections, that we propose here for the first time. The direct consequence is that these microaggregation methods have a higher disclosure risk than believed up to now. (c) 2008 Elsevier B.V. All rights reserved.

关键词： privacy in statistical databases Disclosure risk Record linkage Microaggregation Data projection

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Rethinking rank swapping to decrease disclosure risk

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DATA & KNOWLEDGE ENGINEERING 2008年第1期64卷 346-364页

作者： Nin, Jordi Herranz, Javier Torra, Vicenc Spanish Natl Res Council CSIC Artificial Intelligence Res Inst Bellaterra 08193 Catalonia Spain

Nowadays, the need for privacy motivates the use of methods that allow to protect a microdata file both minimizing the disclosure risk and preserving the data utility. A very popular microdata protection method is rank swapping. Record linkage is the standard mechanism used to measure the disclosure risk of a microdata protection method. In this paper we present a new record linkage method, specific for rank swapping, which obtains more links than standard ones. The consequence is that rank swapping has a higher disclosure risk than believed up to now. Motivated by this, we present two new variants of the rank swapping method, which make the new record linkage technique unsuitable. Therefore, the real disclosure risk of these new methods is lower than the standard rank swapping. (c) 2007 Elsevier B.V. All rights reserved.

关键词： privacy in statistical databases statistical disclosure risk record linkage rank swapping

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Peer-to-Peer Private Information Retrieval

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International Conference on privacy in statistical databases

作者： Domingo-Ferrer, Josep Bras-Amoros, Maria Univ Rovira & Virgili UNESCO Chair Data Privacy Dept Comp Engn & Math E-43007 Tarragona Spain

ISBN: (纸本)9783540874706

关键词： privacy in statistical databases private information retrieval combinatorial designs

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ONN the use of neural networks for data privacy 1

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34th Conference on Current Trends in Theory and Practice of Computer Science

作者： Pont-Tuset, Jordi Medrano-Gracia, Pau Nin, Jordi Larriba-Pey, Josep-L. Muntes-Mulero, Victor Univ Politecn Cataluna Comp Architecture Dept DAMA UPC Campus Nord UPC ES-08034 Barcelona Spain CSIC IIIA Spanish Natl Res Council Artificial Intelligence Res Inst Bellaterra 08193 Spain

ISBN: (数字)9783540775669

ISBN: (纸本)9783540775652

The need for data privacy motivates the development of new methods that allow to protect data minimizing. the disclosure risk without losing valuable statistical information. In this paper, we propose a new protection method for numerical data called Ordered Neural Networks (ONN). ONN presents a new way to protect data based on the use of Artificial Neural Networks (ANNs). The main contribution of ONN is a new strategy for preprocessing data so that the ANNs are not capable of accurately learning the original data set. Using the results obtained by the ANNs, ONN generates a new data set similar to the original one without disclosing the real sensible values. We compare our method to the best methods presented in the literature, using data provided by the US Census Bureau. Our experiments show that ONN outperforms the previous methods proposed in the literature, proving that the use of ANNs is convenient to protect the data efficiently without losing the statistical properties of the set.

关键词： perturbative protection methods data preprocessing artificial neural networks privacy in statistical databases

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Improving Microaggregation for Complex Record Anonymization

Improving Microaggregation for Complex Record Anonymization

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5th International Conference on Modeling Decisions for Artificial Intelligence

作者： Pont-Tuset, Jordi Nin, Jordi Medrano-Gracia, Pau Larriba-Pey, Josep Ll. Muntes-Mulero, Victor Univ Politecn Cataluna DAMA Comp Architecture Dept Campus Nord UPCC Jordi Girona 1-3 ES-08034 Barcelona Catalonia Spain

ISBN: (纸本)9783540882688

Microaggregation is one of the most commonly employed microdata protection methods. This method builds clusters of at least k original records and replaces the records in each cluster with the centroid of the cluster. Usually, when records are complex, i.e., the number of attributes of the data set, is large, this data set is split into smaller blocks of attributes and microaggregation is applied to each block, successively and independently. In this way, the information loss when collapsing several values to the centroid of their group is reduced, at the cost of losing the k-anonymity property when at least;two attributes of different blocks are known by the intruder. In this work, we present a new microaggregation method called One dimension microaggregation (Mic1D - kappa). This method gathers all the values of the data set into a single sorted vector, independently of the attribute they belong to. Then, it microaggregates all the mixed values together. Our experiments show that, using real data, our proposal obtains lower disclosure risk than previous approaches whereas the information loss is preserved.

关键词： Microaggregation k-anonymity privacy in statistical databases

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Ordered data set vectorization for linear regression on data privacy

Ordered data set vectorization for linear regression on data...

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4th International Conference on Modeling Decisions for Artificial Intelligence

作者： Medrano-Gracia, Pau Pont-Tuset, Jordi Nin, Jordi Muntes-Mulero, Victor Univ Politecn Cataluna Comp Architecture Dept DAMA UPC Campus Nord UPCC Jordi Girona 1-3 Barcelona 08034 Spain Spanish Natl Res Council CSIC Artificial Intelligence Res Inst Bellaterra Spain

ISBN: (纸本)9783540737285

Many situations demand from publishing data without revealing the confidential information in it. Among several data protection methods proposed in the literature, those based on linear regression are widely used for numerical data. The main objective of these methods is to minimize both the disclosure risk (DR) and the information lost (IL). However, most of these techniques try to protect the non-confidential attributes based on the values of the confidential attributes in the data set. In this situation, when these two sets of attributes are strongly correlated, the possibility of an intruder to reveal confidential data increases, making these methods unsuitable for many typical scenarios. In this paper we propose a new type of methods called LiROP-k methods that, based on linear regression, avoid the problems derived from the correlation between attributes in the data set. We propose the vectorization, sorting and partitioning of all values in the attributes to be protected in the data set, breaking the semantics of these attributes inside the record. We present two different protection methods: a synthetic protection method called LiROPs-k and a perturbative method, called LiROPp-k. We show that, when the attributes in the data set are highly correlated, our methods present lower DR than other protection methods based on linear regression.

关键词： privacy in statistical databases privacy preserving data mining statistical disclosure risk linear regression masking methods.

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