Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing

Sei, Yuichi; Ohsuga, Akihiko

doi:10.1109/tifs.2016.2632069

Cited by 57 publications

(45 citation statements)

References 23 publications

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“…A number of studies have recently been made on LPDE (e.g., [3,17,25,30,31,37,40,44]), mainly from the perspective of privacy metrics, obfuscation mechanisms, and statistical inference methods. A representative privacy metric in the local model is LDP (Local Differential Privacy) [11].…”

Section: Introductionmentioning

confidence: 99%

Toward Distribution Estimation under Local Differential Privacy with Small Samples

Murakami

Hino

Sakuma

2018

Proceedings on Privacy Enhancing Technologies

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A number of studies have recently been made on discrete distribution estimation in the local model, in which users obfuscate their personal data (e.g., location, response in a survey) by themselves and a data collector estimates a distribution of the original personal data from the obfuscated data. Unlike the centralized model, in which a trusted database administrator can access all users’ personal data, the local model does not suffer from the risk of data leakage. A representative privacy metric in this model is LDP (Local Differential Privacy), which controls the amount of information leakage by a parameter ∈ called privacy budget. When ∈ is small, a large amount of noise is added to the personal data, and therefore users’ privacy is strongly protected. However, when the number of users ℕ is small (e.g., a small-scale enterprise may not be able to collect large samples) or when most users adopt a small value of ∈, the estimation of the distribution becomes a very challenging task. The goal of this paper is to accurately estimate the distribution in the cases explained above. To achieve this goal, we focus on the EM (Expectation-Maximization) reconstruction method, which is a state-of-the-art statistical inference method, and propose a method to correct its estimation error (i.e., difference between the estimate and the true value) using the theory of Rilstone et al. We prove that the proposed method reduces the MSE (Mean Square Error) under some assumptions.We also evaluate the proposed method using three largescale datasets, two of which contain location data while the other contains census data. The results show that the proposed method significantly outperforms the EM reconstruction method in all of the datasets when ℕ or ∈ is small.

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Section: Introductionmentioning

confidence: 99%

Toward Distribution Estimation under Local Differential Privacy with Small Samples

Murakami

Hino

Sakuma

2018

Proceedings on Privacy Enhancing Technologies

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“…There has been considerable research into the implementation of privacy-based MC algorithms. In [20], Yuichi Sei et al proposed a new anonymized datacollection scheme that can estimate data distributions more accurately. They prove that their proposed method can reduce the mean squared error and the Jensen-Shannon divergence compared with other existing studies [20].…”

Section: Related Workmentioning

confidence: 99%

“…In [20], Yuichi Sei et al proposed a new anonymized datacollection scheme that can estimate data distributions more accurately. They prove that their proposed method can reduce the mean squared error and the Jensen-Shannon divergence compared with other existing studies [20]. In [17], Jian Lin et al proposed the BidGuard scheme for privacy-preserving MC incentive mechanisms.…”

Section: Related Workmentioning

confidence: 99%

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A new differential privacy preserving crowdsensing scheme based on the Owen value

Kim

2019

J Wireless Com Network

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The Internet of Everything (IoE) paradigm makes the Internet more pervasive, interconnecting every devices of everyday life, and it is a promising solution for the development of 5G network services. Nowadays, Internetconnected devices are equipped with various built-in sensors. Therefore, the concept of mobile crowdsensing (MC) has been introduced to the IoE-driven situation where mobile devices gather data with the aim of performing a specific application. In this paper, we propose a new cooperative game model for the privacy-driven device collaboration in the MC system. The major goal of our approach is to incentivize the participating devices for effective data acquisitions while protecting each individual privacy based on each device's preference. According to the Owen value mechanism, the proposed scheme provides an effective payment solution for each MC participating device under privacy considered IoE environments. The main merit possessed by our MC control approach is to guide the cooperation of mobile devices in providing MC services. Performance evaluation reveals the superiority of our proposed scheme in terms of task success ratio, MC participating ratio, and payoff fairness. Finally, we provide the guidance on the future research direction of the MC system including other issues.

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“…Under the data collection scenario of single binary attribute and multiple polychotomous attributes, Wang et al studied the relationship between Laplace mechanism and RR by comparing their theoretical utility error and showing the ε ‐differential privacy satisfaction results of RR. Sei and Ohsuga proposed the protocols S2 M and S2Mb, which require less number of samples in estimation, to achieve the privacy‐preserving mobile crowdsensing. Holohan et al presented a systematic study on the optimality of RR in the context of strict and relaxed DP, especially for binary outputs.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Locally private Jaccard similarity estimation

Yan

Ren

et al. 2018

Concurrency and Computation

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Summary Jaccard Similarity has been widely used to measure the distance between two sets (or preference profiles) owned by two different users. Yet, in the private data collection scenario, it requires the untrusted curator could only estimate an approximately accurate Jaccard similarity of the involved users but without being allowed to access their preference profiles. This paper aims to address the above requirements by considering the local differential privacy model. To achieve this, we initially focused on a particular hash technique, MinHash, which was originally invented to estimate the Jaccard similarity efficiently. We designed the PrivMin algorithm to achieve the perturbation of MinHash signature by adopting Exponential mechanism and build the Locally Differentially Private Jaccard Similarity Estimation (LDP‐JSE) protocol for allowing the untrusted curator to approximately estimate Jaccard similarity. Theoretical and empirical results demonstrate that the proposed protocol can retain a highly acceptable utility of the estimated similarity as well as preserving privacy.

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Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing

Cited by 57 publications

References 23 publications

Toward Distribution Estimation under Local Differential Privacy with Small Samples

Toward Distribution Estimation under Local Differential Privacy with Small Samples

A new differential privacy preserving crowdsensing scheme based on the Owen value

Locally private Jaccard similarity estimation

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