L-diversity: privacy beyond k-anonymity

Machanavajjhala, Ashwin; Gehrke, Johannes; Kifer, Daniel; Venkitasubramaniam, Muthuramakrishnan

doi:10.1109/icde.2006.1

Cited by 2,583 publications

(2,849 citation statements)

References 60 publications

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“…Cloaking can be done based on various metrics like l diversity, k anonymity etc. there can be more than one cloaking regions for a user based on different schemes [8].…”

Section: Introductionmentioning

confidence: 99%

Secure Cloaking Area Based on User Profile Similarity

Jagwani¹,

Kaushik²

2016

IJET

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Abstract-Location-based services (LBS) are the applications of mobile technology, GIS and Internet that utilize the location of the user in order to provide the service. However, access to a person's location might allow the service provider to breach the privacy of the person. Location privacy based on k-anonymity addresses this threat by cloaking the person's location such that there are at least k -1 other people within the cloaked area. Then instead of the exact location, cloaking area will be revealed to service provider. In this paper it has been shown that only k anonymity is not sufficient to protect location privacy. Problem of location privacy preservation is addressed via technique of anonymizing similar users together. The proposed work aims at generating a cloaking area which is containing users of similar profile. The cloaking area generated as a result is more secure. Further, for the validity of this claim statistical techniques have been applied and it has been proved that cloaking area having more similar users is safer. Experimental evaluations have been performed to calculate the area of cloaking region and time required to generate it. It has been found that for enhanced privacy levels our system puts up a little overhead.

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“…Cloaking can be done based on various metrics like l diversity, k anonymity etc. there can be more than one cloaking regions for a user based on different schemes [8].…”

Section: Introductionmentioning

confidence: 99%

Secure Cloaking Area Based on User Profile Similarity

Jagwani¹,

Kaushik²

2016

IJET

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“…To ensure privacy of released data, there have been some developed methodology trying to achieve this goal, and those techniques are often labeled as "data de-identification". The more mentioned ones includes K-anonymity [6] and its derivatives [7,8], differential privacy [16], and other attempts from statistical discipline [9]. Due to its deployment by major companies such as Apple 2 and Google, here we'll discuses difteriential privacy as a potential solution to ensure privacy on IoT data release.…”

Section: Ensuring Privacy When Releasing Data To a Third Party Or Thementioning

confidence: 99%

De-Identification Technique for Iot Wireless Sensor Network Privacy Protection

Huang¹,

Li²,

Tai³

et al. 2017

Jurnal Ilmu Komputer dan Informasi

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As the IoT ecosystem becoming more and more mature, hardware and software vendors are trying create new value by connecting all kinds of devices together via IoT. IoT devices are usually equipped with sensors to collect data, and the data collected are transmitted over the air via different kinds of wireless connection. To extract the value of the data collected, the data owner may choose to seek for third-party help on data analysis, or even of the data to the public for more insight. In this scenario it is important to protect the released data from privacy leakage. Here we propose that differential privacy, as a de identification technique, can be a useful approach to add privacy protection to the data released, as well as to prevent the collected from intercepted and decoded during over-the-air transmission. A way to increase the accuracy of the count queries performed on the edge cases in a synthetic database is also presented in this research. Keywords: differential privacy, internet of things, sensor network AbstrakSebagai ekosistem IOT menjadi lebih dan lebih dewasa, vendor hardware dan software berusaha menciptakan nilai baru dengan menghubungkan semua jenis perangkat bersama melalui IOT. Perangkat IOT biasanya dilengkapi dengan sensor untuk mengumpulkan data, dan data yang dikumpulkan ditransmisikan melalui udara melalui berbagai jenis koneksi nirkabel. Untuk mengekstrak nilai data yang dikumpulkan, pemilik data dapat memilih untuk meminta bantuan dari pihak ketiga dalam analisis data, atau bahkan data kepada publik untuk wawasan yang lebih dalam. Dalam skenario ini penting untuk melindungi data yang dirilis dari kebocoran privasi. Di sini kami mengusulkan bahwa privasi diferensial, sebagai teknik identifikasi de, dapat menjadi pendekatan yang berguna untuk menambah perlindungan privasi data yang dirilis, serta untuk mencegah diambil dan diterjemahkan selama transmisi over-the-air. Sebuah cara untuk meningkatkan akurasi query count dilakukan pada kasus tepi dalam database sintetis juga disajikan dalam penelitian ini. Kata Kunci: privasi differensial, internet of things, jaringan sensor

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“…This TTP is also a performance bottleneck since all the submitted LBS queries have to go through it. Moreover, these methods are vulnerable to background knowledge attacks and homogeneity attacks [20].…”

Section: Location Privacy Protectionmentioning

confidence: 99%

“…Based on this principle, numerous privacy metrics have been proposed for quantifying the capability of the adversary. Location k-anonymity [3] and its variation like l-diversity [20] and t-closeness [21] are proposed to measure the ability of the adversary to differentiate the real user from others within the anonymity set. To overcome the drawbacks of k-anonymity in quantifying location privacy, entropy-based metrics have been adopted in [5,13,22,23] for quantifying the information an adversary can obtain from one (or a series) of location update(s).…”

Section: Location Privacy Metricsmentioning

confidence: 99%

A Context‐Aware Location Differential Perturbation Scheme for Privacy‐Aware Users in Mobile Environment

Zhang

Huang

et al. 2018

Wireless Communications and Mobile Computing

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The proliferation of location-based services, representative services for the mobile networks, has posed a serious threat to users' privacy. In the literature, several privacy mechanisms have been proposed to preserve location privacy. Location obfuscation enforced using cloaking region is a widely used technique to achieve location privacy. However, it requires a trusted third-party (TTP) and cannot sufficiently resist various inference attacks based on background information and thus is vulnerable to location privacy breach. In this paper, we propose a context-aware location privacy-preserving solution with differential perturbations, which can enhance the user's location privacy without requiring a TTP. Our scheme utilizes the modified Hilbert curve to project every 2-d location of the user in the considered map to 1-d space and randomly generates the reasonable perturbation by adding Laplace noise via differential privacy. In order to solve the resource limitation of mobile devices, we use a quad-tree based scheme to transform and store the user context information as bit stream which achieves the high compression ratio and supports efficient retrieval. Security analysis shows that our proposed scheme can effectively preserve the location privacy. Experimental evaluation shows that our scheme retrieval accuracy is increased by an average of 15.4% compared with the scheme using standard Hilbert curve. Our scheme can provide strong privacy guarantees with a bounded accuracy loss while improving retrieval accuracy.

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L-diversity: privacy beyond k-anonymity

Abstract: Abstract

Cited by 2,583 publications

References 60 publications

Secure Cloaking Area Based on User Profile Similarity

Secure Cloaking Area Based on User Profile Similarity

De-Identification Technique for Iot Wireless Sensor Network Privacy Protection

A Context‐Aware Location Differential Perturbation Scheme for Privacy‐Aware Users in Mobile Environment

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