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

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

doi:10.1007/s12083-021-01078-6

Cited by 18 publications

(7 citation statements)

References 35 publications

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“…The relevant technologies involved in this paper include trajectory differential privacy protection [ 8 , 9 , 10 ] and location recommendation mechanism [ 11 , 12 , 13 ]. Therefore, the typical methods of trajectory differential privacy protection and location recommendation mechanism are analyzed, respectively.…”

Section: Related Workmentioning

confidence: 99%

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

Zhang

Ding

et al. 2022

Entropy

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The publication of trajectory data provides critical information for various location-based services, and it is critical to publish trajectory data safely while ensuring its availability. Differential privacy is a promising privacy protection technology for publishing trajectory data securely. Most of the existing trajectory privacy protection schemes do not take into account the user’s preference for location and the influence of semantic location. Besides, differential privacy for trajectory protection still has the problem of balance between the privacy budget and service quality. In this paper, a semantics- and prediction-based differential privacy protection scheme for trajectory data is proposed. Firstly, trajectory data are transformed into a prefix tree structure to ensure that they satisfy differential privacy. Secondly, considering the influence of semantic location on trajectory, semantic sensitivity combined with location check-in frequency is used to calculate the sensitivity of each position in the trajectory. The privacy level of the position is classified by setting thresholds. Moreover, the corresponding privacy budget is allocated according to the location privacy level. Finally, a Markov chain is used to predict the attack probability of each position in the trajectory. On this basis, the allocation of the privacy budget is further adjusted and its utilization rate is improved. Thus, the problem of the balance between the privacy budget and service quality is solved. Experimental results show that the proposed scheme is able to ensure data availability while protecting data privacy.

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Section: Related Workmentioning

confidence: 99%

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

Zhang

Ding

et al. 2022

Entropy

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“…The continuous releasing of correlated data and their statistics has the potential for significant social benefits. However, privacy concerns hinder the wider use of these continuous correlated data [ 92 , 93 ]. Therefore, the corresponding GDP mechanism from the perspective of continuous multi-symbol information-theoretic channel needs to be studied by combining the joint probability or Markov chain for continuous correlated data releasing with DP.…”

Section: Open Problemsmentioning

confidence: 99%

GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel

Liu

Peng

Tian

et al. 2022

Entropy

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The existing work has conducted in-depth research and analysis on global differential privacy (GDP) and local differential privacy (LDP) based on information theory. However, the data privacy preserving community does not systematically review and analyze GDP and LDP based on the information-theoretic channel model. To this end, we systematically reviewed GDP and LDP from the perspective of the information-theoretic channel in this survey. First, we presented the privacy threat model under information-theoretic channel. Second, we described and compared the information-theoretic channel models of GDP and LDP. Third, we summarized and analyzed definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP under their channel models. Finally, we discussed the open problems of GDP and LDP based on different types of information-theoretic channel models according to the above systematic review. Our main contribution provides a systematic survey of channel models, definitions, privacy-utility metrics, properties, and mechanisms for GDP and LDP from the perspective of information-theoretic channel and surveys the differential privacy synthetic data generation application using generative adversarial network and federated learning, respectively. Our work is helpful for systematically understanding the privacy threat model, definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP from the perspective of information-theoretic channel and promotes in-depth research and analysis of GDP and LDP based on different types of information-theoretic channel models.

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“…Hemkumar et al [45] in the year 2021 took up the same lines and studied temporal correlation. They proposed w-event privacy to deal with the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The correlation analysis techniques and feature selection techniques used were not good enough to study complex relationships Correlated differential privacy of big data publication [13] Proposed use of Divide and conquer approach along with machine learning, Used correlated big datasets Traditional correlation analysis technique used could not handle high dimensional data Dependent Differential Privacy [10] Proposed DDP and proved mathematically how it can be derived from DP Lacks practical implementation They study Temporal Privacy Leakage [43] Temporal correlation along with the study of the relationship between data privacy and data utility Other correlation models were not studied for temporal leakages Weighted Hierarchical Graph Mechanism [14] Mechanism offers privacy guarantee in case of negative correlation as well Not applicable to nonlinear queries Temporal Correlation Mechanism [45] Proposed w-event privacy using DP for location statistics and provided results regarding data utility…”

Section: Staɵsɵcal Correlaɵonmentioning

confidence: 99%

Machine learning concepts for correlated Big Data privacy

et al. 2021

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With data becoming a salient asset worldwide, dependence amongst data kept on growing. Hence the real-world datasets that one works upon in today’s time are highly correlated. Since the past few years, researchers have given attention to this aspect of data privacy and found a correlation among data. The existing data privacy guarantees cannot assure the expected data privacy algorithms. The privacy guarantees provided by existing algorithms were enough when there existed no relation between data in the datasets. Hence, by keeping the existence of data correlation into account, there is a dire need to reconsider the privacy algorithms. Some of the research has considered utilizing a well-known machine learning concept, i.e., Data Correlation Analysis, to understand the relationship between data in a better way. This concept has given some promising results as well. Though it is still concise, the researchers did a considerable amount of research on correlated data privacy. Researchers have provided solutions using probabilistic models, behavioral analysis, sensitivity analysis, information theory models, statistical correlation analysis, exhaustive combination analysis, temporal privacy leakages, and weighted hierarchical graphs. Nevertheless, researchers are doing work upon the real-world datasets that are often large (technologically termed big data) and house a high amount of data correlation. Firstly, the data correlation in big data must be studied. Researchers are exploring different analysis techniques to find the best suitable. Then, they might suggest a measure to guarantee privacy for correlated big data. This survey paper presents a detailed survey of the methods proposed by different researchers to deal with the problem of correlated data privacy and correlated big data privacy and highlights the future scope in this area. The quantitative analysis of the reviewed articles suggests that data correlation is a significant threat to data privacy. This threat further gets magnified with big data. While considering and analyzing data correlation, then parameters such as Maximum queries executed, Mean average error values show better results when compared with other methods. Hence, there is a grave need to understand and propose solutions for correlated big data privacy.

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

Cited by 18 publications

References 35 publications

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel

Machine learning concepts for correlated Big Data privacy

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