Differentially Private High-Dimensional Data Publication via Sampling-Based Inference

Chen, Rui; Xiao, Qian; Zhang, Yu; Xu, Jianliang

doi:10.1145/2783258.2783379

Cited by 156 publications

(131 citation statements)

References 25 publications

Supporting

Mentioning

131

Contrasting

Order By: Relevance

“…It is worth mentioning that we compared only the above algorithms since our algorithm adopts a novel local privacy paradigm on high-dimensional data. Other competitors are either for non-local privacy [5], [35], [21] or on low-dimension data [12], [14], [16] and therefore not comparable.…”

Section: Discussionmentioning

confidence: 99%

“…Each set of experiments is run 100 times, and the average running time is reported. To measure accuracy, we used the distance metrics AVD (average variant distance) on the three datasets, as suggested in [5], to quantify the closeness between the estimated joint distribution P (ω) and the origin joint distribution Q(ω). The AVD error is defined as…”

Section: Discussionmentioning

confidence: 99%

“…Hence, local privacy [7], [16], [17] has been proposed to provide local privacy guarantees for distributed users. In addition, local privacy from the end user can ensure the consistency of the privacy guarantees when there are multiple accesses to users' data, in contrast to non-local privacy schemes that has to properly split and assign privacy budgets to different steps [5], [21], [35]. In existing work [15][12] [14], local privacy is implemented with randomized response technique [34].…”

Section: Privacy In Distributed Settingmentioning

confidence: 99%

“…Inspired by [35], [5] but without extra privacy budget on dimension reduction, our dimension reduction based on locally once-for-all privacy-preserved data records consists of the following three steps: 1) Pairwise Correlation Computation. We use mutual information to measure pairwise correlations between attributes.…”

Section: Dimension Reduction Via 2-dimensional Joint Distribution Estmentioning

confidence: 99%

See 3 more Smart Citations

High-Dimensional Crowdsourced Data Distribution Estimation with Local Privacy

Ren

et al. 2016

2016 IEEE International Conference on Computer and Information Technology (CIT)

View full text Add to dashboard Cite

Abstract-High-dimensional crowdsourced data collected from numerous users produces rich knowledge for our society. However, it also brings unprecedented privacy threats to the participants. Local privacy, a variant of differential privacy, is proposed to eliminate privacy concerns. Unfortunately, achieving local privacy on high-dimensional crowdsourced data raises great challenges in terms of both computational efficiency and effectiveness. To this end, based on Expectation Maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms that maintain local privacy. Then, we develop a Locally privacy-preserving high-dimensional data Publication algorithm, LoPub, by taking advantage of our distribution estimation techniques. In particular, both correlations and joint distributions among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus achieving both efficiency and effectiveness in high-dimensional data publication. To the best of our knowledge, this is the first work addressing high-dimensional crowdsourced data publication with local privacy. Extensive experiments on realworld datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed, and confirm that our LoPub scheme can keep average 80% and 60% accuracy over the published approximate datasets in terms of SVM and random forest classification, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Privacy In Distributed Settingmentioning

confidence: 99%

Section: Dimension Reduction Via 2-dimensional Joint Distribution Estmentioning

confidence: 99%

See 2 more Smart Citations

High-Dimensional Crowdsourced Data Distribution Estimation with Local Privacy

Ren

et al. 2016

2016 IEEE International Conference on Computer and Information Technology (CIT)

View full text Add to dashboard Cite

show abstract

“…The second approach proposed by [16] is to produce some subset of the "contingency table", which are called "marginal tables", and to connect them together via probabilistic inference mechanism. Some of the attempts of this approach are PrivBayes [10] and DPTable [11], and in this research we use the latter and improve it with ways to improve accuracy without sacrificing privacy, which we'll describe later. Here we'll first describe a the steps involved in DPTable to generate a synthetic data set that can preserve most of the statistical properties of the original data set [12]: (1) Calculating the pair wise mutual information value between attributes.…”

Section: Differentialmentioning

confidence: 99%

De-Identification Technique for Iot Wireless Sensor Network Privacy Protection

Huang¹,

Li²,

Tai³

et al. 2017

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

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

show abstract

Heterogeneous differential privacy for vertically partitioned databases

Xia

Zhu

Ding

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

Summary Existing privacy‐preserving approaches are generally designed to provide privacy guarantee for individual data in a database, which reduces the utility of the database for data analysis. In this paper, we propose a novel differential privacy mechanism to preserve the heterogeneous privacy of a vertically partitioned database based on attributes. We first present the concept of privacy label, which characterizes the privacy information of the database and is instantiated by the classification. Then, we use an information‐based method to systematically explore the dependencies between all attributes and the privacy label. We finally assign privacy weights to every attribute and design a heterogeneous mechanism according to the basic Laplace mechanism. Evaluations using real datasets demonstrate that the proposed mechanism achieves a balanced privacy and utility.

show abstract

Differentially Private High-Dimensional Data Publication via Sampling-Based Inference

Cited by 156 publications

References 25 publications

High-Dimensional Crowdsourced Data Distribution Estimation with Local Privacy

High-Dimensional Crowdsourced Data Distribution Estimation with Local Privacy

De-Identification Technique for Iot Wireless Sensor Network Privacy Protection

Heterogeneous differential privacy for vertically partitioned databases

Contact Info

Product

Resources

About