Differential Privacy: An Economic Method for Choosing Epsilon

Hsu, Justin; Gaboardi, Marco; Haeberlen, Andreas; Khanna, Sanjeev; Narayan, Arjun; Pierce, Benjamin C.; Roth, Aaron

doi:10.1109/csf.2014.35

Cited by 197 publications

(131 citation statements)

References 39 publications

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“…One of them assumes an online trusted third party, similar to our CSP-centered approach. The research in [20], [12] addresses the issue of how to choose the value of privacy budget ε in practice. The former study argues that even for the same value of ε, the privacy guarantees enforced by differential privacy are different based on the domain of the data attributes and supported query types.…”

Section: Related Workmentioning

confidence: 99%

Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing

Ghinita

Fan

et al. 2016

IEEE Trans. on Mobile Comput.

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Spatial Crowdsourcing (SC) is a transformative platform that engages individuals in collecting and analyzing environmental, social and other spatio-temporal information. SC outsources spatio-temporal tasks to a set of workers, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations. However, current solutions require the workers to disclose their locations to untrusted parties. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We address scenarios with both static and dynamic (i.e., moving) datasets of workers. Experimental results on real-world data show that the proposed technique protects location privacy without incurring significant performance overhead.

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Section: Related Workmentioning

confidence: 99%

Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing

Ghinita

Fan

et al. 2016

IEEE Trans. on Mobile Comput.

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“…In our system, we choose to use the Laplacian mechanism, and the parameters that determine the Laplace distribution will be computed according to the results published in [23].…”

Section: Inputsmentioning

confidence: 99%

“…The reason is, the curator randomly selects the encryption functions with the index and send to the respective parties in a temporal order, the curator itself is not permitted to remember this random numbers. In consequence, the curator can re-assemble the data blocks just For NPDDP algorithm based on the principle of differential privacy, it can be easily proved as in [23] that the privacy of the data can be well preserved within the range of the required accuracy and perturbed error. Therefore, our design goals are achieved.…”

Section: Inputsmentioning

confidence: 99%

Security and privacy for data mining of RFID-enabled product supply chains

Yao

Wei

Zhou

et al. 2016

2016 SAI Computing Conference (SAI)

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Abstract-The e-Pedigree used for verifying the authenticity of the products in RFID-enabled product supply chains plays a very important role in product anti-counterfeiting and risk management, but it is also vulnerable to malicious attacks and privacy leakage. While the radio frequency identification (RFID) technology bears merits such as automatic wireless identification without direct eye-sight contact, its security has been one of the main concerns in recent researches such as tag data tampering and cloning. Moreover, privacy leakage of the partners along the supply chains may lead to complete compromise of the whole system, and in consequence all authenticated products may be replaced by the faked ones! Quite different from other conventional databases, datasets in supply chain scenarios are temporally correlated, and every party of the system can only be semi-trusted. In this paper, a system that incorporates merits of both the secure multi-party computing and differential privacy is proposed to address the security and privacy issues, focusing on the vulnerability analysis of the data mining with distributed EPCIS datasets of e-pedigree having temporal relations from multiple range and aggregate queries in typical supply chain scenarios and the related algorithms. Theoretical analysis shows that our proposed system meets perfectly our preset design goals, while some of the other problems leave for future research.

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“…Differential privacy can be explained in the following: the mechanism adds noises to the query results or the data itself to preserve privacy of individual with a given privacy budget [3]. The dataset is then mapped into a histogram as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Efficient e-health data release with consistency guarantee under differential privacy

Lin

2015

2015 17th International Conference on E-Health Networking, Application &Amp; Services (HealthCom)

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E-health data release, which answers the statistical queries of the Electronic Health Records (EHRs), has been widely adopted in modern health care services. However, since the EHRs contain sensitive information of the patients, the data release procedure may lead to the leakage of the privacy of patients if it is done without necessary protection measures in place. On addressing this, existing research literature introduces differential privacy to provide the necessary privacy guarantee. However, it is not suitable in sensitive e-health environments because it lacks the efficiency for data processing and updating. In this paper, we propose an efficient e-health data release scheme with consistency guarantee under differential privacy. Specifically, we improve the performance of the previous work by designing a new private partition algorithm of histogram and also proposing a heuristic hierarchical query method. We conduct real experiments and compare our scheme with the existing one to show that the proposal is more efficient in terms of data processing and updating. Moreover, we increase the accuracy of data release through consistency and give proof of privacy to show that the proposed algorithm is under -differential privacy.

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Differential Privacy: An Economic Method for Choosing Epsilon

Cited by 197 publications

References 39 publications

Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing

Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing

Security and privacy for data mining of RFID-enabled product supply chains

Efficient e-health data release with consistency guarantee under differential privacy

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