A Novel Personalized Differential Privacy Mechanism for Trajectory Data Publication

Tian, Feng; Zhang, Shuangyue; Lu, Laifeng; Lü, Hai; Gui, Xiaolin

doi:10.1109/nana.2017.47

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…When the correlation between multiple locations in the trajectory is ignored, it is vulnerable to a large number of inference attacks. Lu et al (2017) proposed the algorithm that can protect the location privacy of multiple locations with correlation. They used Hidden Markov similarity metric to quantify the correlation of two locations, and then the Laplace mechanism is used to publish trajectory data.…”

Section: Abulmentioning

confidence: 99%

Trajectory Data Publication Based on Differential Privacy

Zhang

2022

International Journal of Information Security and Privacy

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Analyzing trajectory data can provide people with a higher quality of life. However, publishing trajectory data directly will leak privacy. The authors propose a trajectory data publication method based on differential privacy (TDDP). TDDP method consists of two stages. In the location generalization stage, firstly, the locations at each timestamp are clustered into classes by k-means++ algorithm, and then the representative location of each class is selected by using the exponential mechanism. In the generalized trajectory data publication stage, the authors design a sampling mechanism to form the generalized trajectories. The locations are sampled from the representative locations under different timestamps to form the generalized trajectories. The TDDP method can avoid the generation of non-semantic representative locations and ensure that the generalized trajectories can resist filtering attacks. The experimental results show that the trajectory data released by TDDP method can achieve a good balance between privacy protection and data availability.

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Section: Abulmentioning

confidence: 99%

Trajectory Data Publication Based on Differential Privacy

Zhang

2022

International Journal of Information Security and Privacy

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“…There have also been other efforts to model user-specific privacy requirements [29]- [32]. [29]- [31]. For instance, [29] provides partitioning mechanism to group users with personalized privacy requirements into different ǫ partitions under a non-federated setting.…”

Section: Related Workmentioning

confidence: 99%

Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy

Chathoth,

Jagannatha,

Lee

2021

Preprint

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Internet of Things (IoT) devices are becoming increasingly popular and are influencing many application domains such as healthcare and transportation. These devices are used for real-world applications such as sensor monitoring, real-time control. In this work, we look at differentially private (DP) neural network (NN) based network intrusion detection systems (NIDS) to detect intrusion attacks on networks of such IoT devices. Existing NN training solutions in this domain either ignore privacy considerations or assume that the privacy requirements are homogeneous across all users. We show that the performance of existing differentially private stochastic methods degrade for clients with non-identical data distributions when clients' privacy requirements are heterogeneous. We define a cohortbased (ǫ, δ)-DP framework that models the more practical setting of IoT device cohorts with non-identical clients and heterogeneous privacy requirements. We propose two novel continual-learning based DP training methods that are designed to improve model performance in the aforementioned setting. To the best of our knowledge, ours is the first system that employs a continual learning-based approach to handle heterogeneity in client privacy requirements. We evaluate our approach on real datasets and show that our techniques outperform the baselines. We also show that our methods are robust to hyperparameter changes. Lastly, we show that one of our proposed methods can easily adapt to post-hoc relaxations of client privacy requirements.

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“…Yapılan testler sonucunda, önerilen modelin literatürdeki dört farklı modelden daha yüksek veri faydası sunduğu bildirilmiştir. Tian ve arkadaşları [20], mahremiyet duyarlı yörünge verisi yayınlamada kişiselleştirilmiş diferansiyel mahremiyet yaklaşımını uygulayan yeni bir model önermiştir. Yörünge verisinin karakteristiğini çıkarmak amacıyla Hilbert eğrisinin kullanıldığı çalışmada, önerilen modeli test etmek amacıyla Microsoft'un yayınladığı T-Drive veri kümesinden faydalanılmıştır.…”

Section: Li̇teratür Taramasi (Literature Review)unclassified

Yörünge Verisi Yayınlamada Mahremiyet Duyarlı Yeni Bir Model Önerisi ve Uygulaması

2021

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Bu çalışmada, yörünge verilerinin yayınlanmasında diferansiyel mahremiyeti kullanan yeni bir model önerilmiş, başarıyla geliştirilmiş ve gerçek bir veri kümesi üzerinde test edilmiştir. / In this paper, a new trajectory data publishing model using differential privacy was proposed, developed and tested on a real dataset. Şekil. Önerilen modelin akış şeması /Figure. General flowchart of the proposed model Amaç (Aim)Mahremiyet korumalı yörünge verisi yayınlamak için yeni bir model geliştirilmesi amaçlanmıştır. / It was aimed to develop a new model for privacy preserving trajectory data publishing.

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A Novel Personalized Differential Privacy Mechanism for Trajectory Data Publication

Cited by 12 publications

References 23 publications

Trajectory Data Publication Based on Differential Privacy

Trajectory Data Publication Based on Differential Privacy

Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy

Yörünge Verisi Yayınlamada Mahremiyet Duyarlı Yeni Bir Model Önerisi ve Uygulaması

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