Impact of prior knowledge on privacy leakage in trajectory data publishing

Hemkumar, D.; Sadam, Ravichandra; Somayajulu, Durvasula V. L. N.

doi:10.1016/j.jestch.2020.06.002

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…Identity linkage attack: In the published dataset, if the trajectory of the sensor data for a patient is unique, then an adversary can quickly identify a patient record along with the patient's sensitive data using his prior knowledge [11].…”

Section: Introductionmentioning

confidence: 99%

“…Attribute linkage attack: In the published dataset, the most frequent occurrence of the sensitive values of a targeted victim could result in an attribute linking attack. The adversary could breach the sensitive information with high confidence even though the unique sensor trajectory information of the victim is not available [11].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the patient with PId 1 has the correlated records in row 1 and row 4, as shown in Table I. Having additional knowledge about the correlated records by the adversary can predict both trajectory data and the sensitive value of the victim with high confidence [11].…”

Section: Introductionmentioning

confidence: 99%

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Reciprocal Bucketization (RB) - An Efficient Data Anonymization Model for Smart Hospital Data Publishing

2024

IJACSA

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With the lightning growth of the Internet of Things (IoT), enormous applications have been developed to serve industries, the environment, society, etc. Smart Health care is one of the significant applications of the IoT, where intelligent environments enrich safety and ease of surveillance. The database of the Smart Hospital records the patient's sensitive information, which could face various potential privacy breaches through linkage attacks. Publishing such sensitive data to society is challenging in adopting the best privacy preservation model to defend against linkage attacks. In his paper, we propose a novel Reciprocal Bucketization Anonymization model as the privacy preservation method to defend against Identity, Attribute, and Correlated Linkage attacks. The proposed anonymization method creates the Buckets of patient records and then partitions the data into sensor trajectory and Multiple Sensitive attributes (MSA). A local suppression is employed on Sensor Trajectory Data and Slicing on MSA to get the anonymized data to be published gathered by combining anonymized sensor trajectory and MSA. The proposed method is validated on the synthetic and real-time dataset by comparing its data utility loss in both sensor trajectory and the MSA. The experimental results eradicate that the RB -Anonymization exhibits the nature of best privacy preservation against Identity, Attribute, and Correlated linkages attacks with negligible utility loss compared with the existing methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reciprocal Bucketization (RB) - An Efficient Data Anonymization Model for Smart Hospital Data Publishing

2024

IJACSA

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“…However, the attacker can still infer the sensitive attribute range based on the equivalence class where the quasi-identifier is located. 4) Suppression [21], [22]. The released locations are selectively suppressed or reduced frequency according to the region's sensitivity.…”

mentioning

confidence: 99%

A Trajectory Released Scheme for the Internet of Vehicles Based on Differential Privacy

Cai

Lyu

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The locations and users' information can be shared and interacted in the IoV (Internet of Vehicles), which provides sufficient data for traffic deployment and behavior pattern analysis. However, privacy issues had become more severe since personal or sensitive information is inclined to be revealed in a big data environment. In this work, a novel differential privacybased algorithm named DPTD (Differentially Private Trajectory Database) is proposed for trajectory database releasing. Firstly, a 3-dimensional generalized trajectory dataset is established by considering the time factor. Then, the trajectory space is divided into several planes through the timestamps, and the set of the locations on each plane is further processed by clustering and generalizing to re-form new trajectories, that is, the trajectories to be released. This method is quite favorable to prefix-tree releasing because the spatiotemporal characteristics of the trajectories can be captured and spareness problem is fixed. Besides, a Markov assumption-based prediction method is suggested in order to reduce the cost of adding noise. Unlike the traditional method that the noise is added layer by layer, the noise is only added to the odd layers based on the prediction through spatiotemporal correlation, saving approximately 50% of the privacy budget. Theoretical analysis and experimental results show that the proposed algorithm has better data availability than the compared algorithms while guaranteeing the expected privacy level.

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Impact of data correlation on privacy budget allocation in continuous publication of location statistics

Hemkumar

Sadam

Somayajulu

2021

Peer-to-Peer Netw. Appl.

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Impact of prior knowledge on privacy leakage in trajectory data publishing

Cited by 7 publications

References 25 publications

Reciprocal Bucketization (RB) - An Efficient Data Anonymization Model for Smart Hospital Data Publishing

Reciprocal Bucketization (RB) - An Efficient Data Anonymization Model for Smart Hospital Data Publishing

A Trajectory Released Scheme for the Internet of Vehicles Based on Differential Privacy

Impact of data correlation on privacy budget allocation in continuous publication of location statistics

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