Towards a More Reasonable Generalization Cost Metric for K-Anonymization.

At the same time, we apply the partitioning-based and k-means clustering-based two k-anonymity algorithms to the soccer fitness data publishing to trade-offs the data utility and the personal privacy. ... With the development of data mining technology, more and more researchers use the soccer fitness data to analyse the ranking of soccer athletes' and professional training. ... The reason for this is that as k increases, more and more records are generalized to have the same quasi-identifier value, resulting in greater loss of information. ...

doi:10.1051/matecconf/201818903007 fatcat:y5f4ld6serbmbpta3hf6fk3cmq

DOAJ

Concretely, we use a k-anonymity model to preserve privacy while discovering and maintaining association rules through a novel algorithm, M3AR-member migration technique for maintaining association rules ... Instead, we propose a member migration technique that is more appropriate for the requirements of maintaining association rules. ... However, we do not use those techniques for some reasons: the attribute level generalization has the disadvantage of creating a lot of data distortion. ...

doi:10.1109/ntms.2011.5720635 dblp:conf/ntms/DangKQ11 fatcat:qks4zvbsl5ex3ek4pjpni27v34

We show that existing anonymization techniques are inappropriate for situations where δ-presence is a good metric (specifically, where knowing an individual is in the database poses a privacy risk), and ... We present a metric, δ-presence, that clearly links the quality of anonymization to the risk posed by inadequate anonymization. ... For cost metrics proposed so far, a high level generalization (e.g., T ) is more costly than a lower generalization (e.g., T ). ...

doi:10.1145/1247480.1247554 dblp:conf/sigmod/NergizAC07 fatcat:czkhi7tlczayfexz3qjymo3tku

Through experiments on real census data, we show that more-flexible generalization schemes produce higher-quality anonymizations and the bottom-up works better for small k values and small number of quasi-identifer ... A major thread of research in this area aims at developing more flexible generalization schemes and more efficient searching algorithms to find better anonymizations (i.e., those that have less information ... For the same k value, the cost decreases for the more sophisticated generalization schemes. ...

doi:10.1016/j.datak.2007.06.015 fatcat:un2ulu3x5jdllfg54bi7wjadz4

We present a utility metric that maximizes the probability of a good representation and propose trajectory anonymization techniques to address time and space sensitive applications. ... We provide privacy protection by defining trajectory k-anonymity, meaning every released information refers to at least k users/trajectories. ... Most clustering algorithms can easily be modified for k-anonymity by enforcing that the size of the clusters should be more than k (2, 33, 12, 1) . ...

doi:10.1145/1503402.1503413 dblp:conf/gis/NergizAS08 fatcat:7zcpieahunfw5nygpyso3jlthm

In the real world, companies would publish social networks to a third party, e.g., a cloud service provider, for marketing reasons. ... With this information, an attacker may re-identify the target from a k-anonymity social network with a probability higher than 1/k, where any node's 1-neighborhood graph is isomorphic with k − 1 other ... To obtain a reasonable randomization probability p, we first randomly generate a graph with N nodes and M edges. ...

doi:10.1109/infcom.2013.6567099 dblp:conf/infocom/WangLLY013 fatcat:ihwxn5snlffm7deecrhytrgtxu

Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on k-anonymity algorithms ... Furthermore, we design and implement a differentiated k-anonymity l-diversity social network anonymity algorithm, which seeks to protect users' privacy in social networks and increase the usability of ... Figure 4 . 4 Last Figure 5 . 5 Last.fm dataset (a) and Delicious dataset (b): costs for different k values. ...

doi:10.3390/a9040085 fatcat:yg5knfrcczc4fg5kp4uitauh74

DOAJ Szczepanski

In view of this challenge, we propose a two-tier schema for the privacy preservation based on k−anonymity principle meanwhile reduce the cost for privacy protection. ... In recent years, with the development of mobile devices, the location based services (LBSs) have become more and more prevailing and most applications installed on these devices call for location information ... Other metrics, such as k-anonymity [14] , t-closeness [15] , [16] , and other variations, measure the privacy level according to the size of anonymity set. The above metrics are uncertainty based. ...

doi:10.1109/tsusc.2017.2733018 dblp:journals/tsusc/FeiLDHDN19 fatcat:mf5lvhqmyvd5ppo4oyttaubari

K-Anonymity is a property for the measurement, management, and governance of the data anonymization. ... Many implementations of k-anonymity have been described in state of the art, but most of them are not able to work with a large number of attributes in a "Big" dataset, i.e., a dataset drawn from Big Data ... As discussed in section 2, there are two metrics for the evaluation of a single node, namely, (a) k-anonymity and (b) loss of information. ...

arXiv:2204.01533v1 fatcat:xbdsrwcbarfvjbscwz52rbecqa

Open Access

We have also discussed various attacks that may take place during anonymization. A comprehensive study of various metric used for measuring anonymity has also been discussed. ... In this paper, we provide a review of various methods for anonymization and analyze various disclosures that may happen in each of them. ... Iloss for the entire T* is given by ∑ ∈ Classification Metric (CM), is introduced by Iyengar [18] to optimize a k-anonymous dataset. ...

doi:10.5120/8521-2380 fatcat:fwpjo3yk2jftrhsfsg7klaa44y

Based on the proposed k-anonymity scheme, the trade-offs between the achieved query-anonymity and various performance measures including, communication-cost, return-on-investment metric, path-length, and ... Thus, the paper firstly introduces a novel query k-anonymity scheme that countermeasures such a privacy threat. ... A reason for that must be related to the way that the piggy-back strategy collects sensor readings which incurs a cost that grows with the remainder of n/k. ...

doi:10.33969/j-nana.2021.010302 fatcat:wntb6bjbozd35mr5uq6ieo6u4i

In this paper, we propose a k-anonymization solution for classification. ... Previous work attempted to find an optimal k-anonymization that minimizes some data distortion metric. ... For this reason, our problem does not have a closed form cost metric, and an "optimal" solution to our problem is not necessarily an optimal k-anonymization based on a closed form cost metric, and vice ...

doi:10.1109/tkde.2007.1015 fatcat:op6drxswhzhk5hzursulhkzr3u

In this paper, we propose a new semantic privacy preservation method rely on the well-established k-anonymity and l-diversity privacy metrics for semantic cloaking. ... We also define a representative cloaking region which helps in communication cost reduction caused by user movement. ... Numerous privacy metric and scheme have been proposed in LBS privacy protection community including [1, 4] :  Query Privacy Metric -k-anonymity is the most popular metric used for LBS query privacy protection ...

doi:10.14257/ijsia.2015.9.2..01 fatcat:evrffzbnu5a2pnmoi7yt6nevka

Finally, we show that the anonymizations generated by the R-tree approach do not sacrifice quality in their search for efficiency; in fact, by several previously proposed quality metrics, the compact partitioning ... In this paper we observe that k-anonymizing a data set is strikingly similar to building a spatial index over the data set, so similar in fact that classical spatial indexing techniques can be used to ... It is also reasonable to expect the execution costs for the compaction process to be relatively small when compared to actual anonymization costs as its basic operation is a single pass over each partition ...

doi:10.1109/icde.2007.369024 dblp:conf/icde/IwuchukwuDDN07 fatcat:semsw5i2gfhvnjanzp66bl7syi

Experimental results show that mix optimization may lead to substantial delay reductions for a desirable level of anonymity. ... Anonymous-communication systems emerge as a response against such traffic analysis threats. Mixes, and in particular threshold pool mixes, are a building block of anonymous communications systems. ... Acknowledgments We would like to thank the anonymous referees for their extremely valuable comments. This work was partly supported by the Spanish Government through projects ...

doi:10.1016/j.comnet.2014.04.007 fatcat:7fgnqqdionbwlljefng75hcrha

K-anonymity model for privacy-preserving soccer fitness data publishing

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Protecting Privacy While Discovering and Maintaining Association Rules

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Hiding the presence of individuals from shared databases

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Towards optimal k-anonymization

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Towards trajectory anonymization

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Outsourcing privacy-preserving social networks to a cloud

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A Differentiated Anonymity Algorithm for Social Network Privacy Preservation

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A K-Anonymity Based Schema for Location Privacy Preservation

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Real-world K-Anonymity Applications: the KGen approach and its evaluation in Fraudulent Transactions [article]

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A Survey on Methods, Attacks and Metric for Privacy Preserving Data Publishing

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k-Anonymous Query Scheme on the Internet of Things: a Zero Trust Architecture

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Anonymizing Classification Data for Privacy Preservation

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A Novel Location Privacy Preservation Method for Moving Object

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K-Anonymization as Spatial Indexing: Toward Scalable and Incremental Anonymization

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Optimizing the design parameters of threshold pool mixes for anonymity and delay

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