Differentially private transit data publication

Chen, Rui; Fung, Benjamin C. M.; Desai, Bipin C.; Sossou, Nériah M.

doi:10.1145/2339530.2339564

Cited by 202 publications

(70 citation statements)

References 31 publications

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“…Furthermore, we designed a synthetic sequential database construction method, which allows published n-grams to be used for a wider range of data analysis tasks. Extensive experiments on real-life datasets proved that our solution substantially outperforms state-of-the-art techniques [16], [4] in terms of count query and frequent sequential pattern mining. …”

Section: Resultsmentioning

confidence: 89%

“…In particular, we demonstrate that our solution substantially outperforms the state-of-the-art techniques [16], [4] in terms of count query and frequent sequential pattern mining.…”

Section: Introductionmentioning

confidence: 87%

“…To our best knowledge, the paper [4] is the only work that applies differential privacy to sequential data release. They make use of the prefix tree structure to group sequences with the identical prefix into the same branch.…”

Section: Related Workmentioning

confidence: 99%

“…N -gram models estimate the probability of the next item for a given sequence by making use of the Markov independence assumption (of order n − 1) that the occurrence of each item in a sequence depends only on the previous n − 1 items (instead of all previous items), where n is typically a small value (e.g., [3][4][5]. Let the probability that a sequence L1 → L2 → .…”

Section: N -Gram Modelmentioning

confidence: 99%

“…As a reference point, for count query, we compare the utility of our solution with the approach proposed in [4], which relies on a prefix tree structure (referred to as Prefix ); for frequent sequential pattern mining, we compare our approach with both Prefix and the method designed in [16] for finding frequent (sub)strings (referred to as FFS ). Two reallife sequential datasets are used in our experiments.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Differentially private sequential data publication via variable-length n-grams

Chen

Ács

Castelluccia

2012

Proceedings of the 2012 ACM Conference on Computer and Communications Security

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231

214

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Sequential data is being increasingly used in a variety of applications. Publishing sequential data is of vital importance to the advancement of these applications. However, as shown by the re-identification attacks on the AOL and Netflix datasets, releasing sequential data may pose considerable threats to individual privacy. Recent research has indicated the failure of existing sanitization techniques to provide claimed privacy guarantees. It is therefore urgent to respond to this failure by developing new schemes with provable privacy guarantees. Differential privacy is one of the only models that can be used to provide such guarantees. Due to the inherent sequentiality and high-dimensionality, it is challenging to apply differential privacy to sequential data. In this paper, we address this challenge by employing a variable-length n-gram model, which extracts the essential information of a sequential database in terms of a set of variable-length n-grams. Our approach makes use of a carefully designed exploration tree structure and a set of novel techniques based on the Markov assumption in order to lower the magnitude of added noise. The published n-grams are useful for many purposes. Furthermore, we develop a solution for generating a synthetic database, which enables a wider spectrum of data analysis tasks. Extensive experiments on real-life datasets demonstrate that our approach substantially outperforms the state-of-the-art techniques.

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Section: Resultsmentioning

confidence: 89%

“…In particular, we demonstrate that our solution substantially outperforms the state-of-the-art techniques [16], [4] in terms of count query and frequent sequential pattern mining.…”

Section: Introductionmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

Section: N -Gram Modelmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

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Differentially private sequential data publication via variable-length n-grams

Chen

Ács

Castelluccia

2012

Proceedings of the 2012 ACM Conference on Computer and Communications Security

Self Cite

231

214

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Differential privacy–based location privacy enhancing in edge computing

Miao

Jing

Song

2018

Concurrency and Computation

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In the era of edge computing, real-time data preprocessing on the edge node has the potential to improve computational efficiency and data accuracy. However, a significant challenge is private data disclosure, particularly in the case of location-based services. To address this challenge, in this paper, by leveraging differential privacy, we propose a privacy-aware framework for mobile edge computing called MEPA to protect the location privacy in which the edge node is regarded as an anonymous central server. The proposed framework can provide computing services without deploying special infrastructure. To be specific, in order to solve the problem of constrained computing resources in the edge nodes, the algorithm of Quadtree Differential Privacy based on Hilbert curve division (QTDP-H) two-dimensional spatial data query transmission is proposed.First, a noise quadtree is established and the privacy budget is divided according to the tree level.Then, the constructed quadtree is represented by quanternary, so that the partition based on Hilbert curve can be established and the two-dimensional data in the area can be converted into one-dimensional, which can greatly improve the retrieval efficiency. The effectiveness of the proposed algorithm in terms of time complexity and retrieval accuracy has been verified by extensive experimental results. Compared with traditional methods of (D, ) − LP, the average runtime can be reduced by 15%-20%, and the average relative error is reduced by 20%. KEYWORDS differential privacy, Hilbert curve, location-based service, mobile edge computing, privacy aware, quadtree INTRODUCTIONWith the development of the Internet of Things (IoT) and cloud computing, the amount of data on the edge network is rapidly growing. Therefore, it is more efficient to process the data at the edge of the network. However, the development of network bandwidth is slower compared with the powerful computing ability of cloud services. The amount of data is growing rapidly, and time consumption in data transmission has become the main challenge that restricts the cloud computing applications. In cloud computing model, the devices at the edge often only act as consumers; however, people often generate data from the devices they use. 1 This shift from data consumers to data consumers/producers requires more functionality on the edge node. However, at the edge of the network, user privacy and data security are among the most important requirements. If IoT is deployed in the home, some privacy information can be obtained from the user data, such as by reading user electric meter and water meter data to determine whether there are people in the room. By obtaining location data, people can estimate someone's home address, lifestyle, social relationships, and more. Therefore, the disclosure of this personal information to attackers can pose a serious threat to the privacy of users. The development of edge computing will promote a variety of intelligent applications, which were impractical in the past due to network ...

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A differential privacy trajectory data storage and publishing scheme based on radix tree

Tian

Zhu

2023

Concurrency and Computation

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SummaryWith the rapid development of information technology, location‐based services (LBSs) are becoming more and more popular, and at the same time, the users' privacy issues are becoming more and more serious. Therefore, this article proposes a differential private trajectory data storage and publishing scheme based on radix tree (TPRT). Firstly, the original trajectory is preprocessed by considering semantic information and simplified by dividing the stay area to reduce the risk of privacy leakage. Then, differential privacy technology is used to add Laplace noise to sensitive locations to achieve generalization. Finally, a radix tree structure satisfying differential privacy is defined, which alleviates the problem of large space consumption caused by tree height. To protect privacy, Laplace noise is added to the sensitive counts of the radix tree and limited by the Markov chain and privacy degree. Compared with several existing methods, our method has better performance and higher data availability.

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Differentially private transit data publication

Cited by 202 publications

References 31 publications

Differentially private sequential data publication via variable-length n-grams

Differentially private sequential data publication via variable-length n-grams

Differential privacy–based location privacy enhancing in edge computing

A differential privacy trajectory data storage and publishing scheme based on radix tree

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