Incremental release of differentially-private check-in data

Riboni, Daniele; Bettini, Cláudio

doi:10.1016/j.pmcj.2014.11.007

Cited by 9 publications

(10 citation statements)

References 15 publications

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“…This massive source of data has yielded new prospective applications in crowd analysis for researchers in different areas, such as marketing and trend detections. Social media data allow researchers to investigate new insights at a higher level of analysis, for example computing individual tracks, the purpose of travel, or connection dependencies in a crowd [53]. On the other hand, analyzing the social connection between users is important to find the impact of a message to create a crowd.…”

Section: Crowd Social Media Analysismentioning

confidence: 99%

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

Ebrahimpour

Wan

Cervantes

et al. 2019

IJGI

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Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed.

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Section: Crowd Social Media Analysismentioning

confidence: 99%

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

Ebrahimpour

Wan

Cervantes

et al. 2019

IJGI

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“…Sparseness and high dimensionality in raw data typically leads to reduction in size in the anonymized output due to the suppression of trajectories, as illustrated in Example 1. [85]. One such research [46] for applying differential privacy to publish trajectory data, aims to generalize the trajectories by generalizing the locations and adding noise to the number of occurrences of trajectories to ensure differential privacy.…”

Section: Challenges and Concernsmentioning

confidence: 99%

“…Next, they introduce another algorithm to publish these generalized trajectories in a differentially-private manner, by generating new trajectories based on the generalized locations, and finally publishing the noisy counts after the addition of Laplace noise. Riboni et al [85] proposed a technique that integrated differential privacy and pre-filtering process, explicitly for protecting check-in data so an untrusted adversary is unable to infer check-in details shared by other individuals. Their approach primarily publishes a single version of the differentially-private data by enforcing (L, j)-density.…”

Section: Publishing Movement Datamentioning

confidence: 99%

“…These techniques, however, are not primarily aimed at differentially-private data. The techniques for incremental release are published in [86] and [113] with each technique relating to a different type of data like set-valued data [113], check-in data [86]. [86] presents the technique for providing differential privacy over check-in data to protect spatio-temporal data from the untrusted third parties who have access to the data, and from other users who might infer locations visited by other users, with the help of pre-filtering process.…”

Section: Incremental Data Publishingmentioning

confidence: 99%

“…This algorithm generates a series of differentially-private releases like in [86]. The algorithm uses Top-down partitioning based on the generated item-free taxonomy tree.…”

Section: Incremental Data Publishingmentioning

confidence: 99%

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Privacy-Preserving Trajectory Data Publishing via Differential Privacy

Dwivedi¹

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Over the past decade, the collection of data by individuals, businesses and government agencies has increased tremendously. Due to the widespread of mobile computing and the advances in location-acquisition techniques, an immense amount of data concerning the mobility of moving objects have been generated. The movement data of an object (e.g. individual) might include specific information about the locations it visited, the time those locations were visited, or both. While it is beneficial to share data for the purpose of mining and analysis, data sharing might risk the privacy of the individuals involved in the data. Privacy-Preserving Data Publishing (PPDP) provides techniques that utilize several privacy models for the purpose of publishing useful information while preserving data privacy. The objective of this thesis is to answer the following question: How can a data owner publish trajectory data while simultaneously safeguarding the privacy of the data and maintaining its usefulness? We propose an algorithm for anonymizing and publishing trajectory data that ensures the output is differentially-private while maintaining high utility and scalability. Our solution comprises a twofold approach. First, we generalize trajectories by generalizing and then partitioning the timestamps at each location in a differentially-private manner. Next, we add noise to the real count of the generalized trajectories according to the given privacy budget to enforce differential privacy. As a result, our approach achieves an overall-differential privacy on the output trajectory data. We perform experimental evaluation on real-life data, and demonstrate that our proposed approach can effectively answer count and range queries, as well as mining frequent sequential patterns. We also vi show that our algorithm is efficient w.r.t. privacy budget and number of partitions, and also scalable with increasing data size.

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Probability Indistinguishable: A Query and Location Correlation Attack Resistance Scheme

Zhang

Yang

et al. 2017

Wireless Pers Commun

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Incremental release of differentially-private check-in data

Cited by 9 publications

References 15 publications

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

Privacy-Preserving Trajectory Data Publishing via Differential Privacy

Probability Indistinguishable: A Query and Location Correlation Attack Resistance Scheme

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