The privacy preserving method for dynamic trajectory releasing based on adaptive clustering

Yu, Xin; Xie, Zhiqiang; Yang, Jing

doi:10.1016/j.ins.2016.10.038

Cited by 42 publications

(20 citation statements)

References 10 publications

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“…First, the publishers set a unified privacy protection level for all trajectories [6][7][8][9][10][11] and then complete the privacy protection for publishing trajectories. Second, trajectory points are taken as atomic operation objects; the publisher sets a unified privacy protection level for all trajectory points [12][13][14][15][16] and then completes the privacy protection. Wang.…”

Section: Related Workmentioning

confidence: 99%

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

Wang

Chen

Zhang

2020

Mathematical Problems in Engineering

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Up to now, a large amount of trajectory data have been collected by trusted servers because of the wide use of location-based services. One can extract useful information via an analysis of trajectory data. However, the privacy of trajectory bodies risks being inadvertently divulged to others. Therefore, the trajectory data should be properly processed for privacy protection before being released to unknown analysts. This paper proposes a privacy protection scheme for publishing the trajectories with personalized privacy requirements based on the translocation of trajectory points. The algorithm not only enables the published trajectory points to meet the personalized privacy requirements regarding desensitization and anonymity but also preserves the positions of all trajectory points. Our algorithm trades the loss in mobility patterns for the advantage in the similarity of trajectory distance. Related experiments on trajectory data sets with personalized privacy requirements have verified the effectiveness and the efficiency of our algorithm.

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Section: Related Workmentioning

confidence: 99%

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

Wang

Chen

Zhang

2020

Mathematical Problems in Engineering

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“…The computation of distance between individual 7 and Q is as follows: r 7q is the identity element of Q ∪ R (7), which is {F, [33,34] We execute line 10 in Algorithm 1. D = D − R(7), Q = Q ∪ R (7), and r q is {F, [33,34], {10070, 10073}, null}. When we check whether Q satisfies EIR 3-diversity, we do not call SatPriIR_El and use the incremental method SatPriIRInc_El.…”

Section: Algorithm 5 Satpriirinc_el(q H Q P L)mentioning

confidence: 99%

“…In recent years, the problem of privacy-preserving data publishing has been studied extensively [1][2][3][4][5][6][7][8][9]. Traditional privacy-preserving approaches deal with static datasets with single record and single sensitive attribute.…”

Section: Anonymization For Static Datasetsmentioning

confidence: 99%

“…Thus, the methods and tools for privacy-preserving data publishing are required. Recently, the problem has received wide attention, so many approaches have been proposed catering for different data publishing scenarios [2][3][4][5][6][7][8][9]. In particular, for relational data, main approaches are k-anonymity [3], l-diversity [10], (α, k)-anonymity [11], and t-closeness [12], all of which assume that an individual has only a record in a data table.…”

Section: Introductionmentioning

confidence: 99%

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Two privacy-preserving approaches for data publishing with identity reservation

Wang

Luo

et al. 2018

Knowl Inf Syst

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Many approaches have been proposed for publishing useful information while preserving data privacy. Among them, the privacy models of identity-reserved (k, l)-anonymity and identityreserved (α, β)-anonymity have been proposed to handle the situation where an individual could have multiple records. However, the two models fail to prevent attribute disclosure. To this end, we propose two new privacy models: enhanced identity-reserved l-diversity and enhanced identity-reserved (α, β)-anonymity. Moreover, to implement the two privacy models we design a general anonymization algorithm, called DAnonyIR, with clustering technique by calling different decision functions, which can decrease the information loss caused by generalization. Further, we compare DAnonyIR concerning our two privacy models with existing generalization method GeneIR concerning identity-reserved (k, l)-anonymity and identity-reserved (α, β)-anonymity, respectively. The experimental results show that our two approaches provide stronger privacy preservation, and their information loss and relative error ratio of query answering are less than those of GeneIR.

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“…In [11] an attacker is assumed to have an unregulated number of intrusion, and presented a monitoring system, which observe the groups, and then has sensitive values for each group defined to monitor. [12][13][14][15][16] defines a anonymity approach where data is defined in easy and accessible format rather than a non-formal recording using a single and generalization hierarchy. [17] Introduces a controlling approach which define the conversion, which share transactions with the maximum knowledge of materials and can be shared with a public privacy.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Slide Window Security Method for Transaction Updation in Data Stream Mining

Kumar,

Raju

2019

IJEAT

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Data steam mining has gained large interest in current research domain. Where various information’s are retrieved based on the content of the context, the accuracy of the input stream with respect to its privacy is a major challenge. Windowing technique is used an effective approach in providing security measure in data stream mining. The recent develop windowing approach operates using sliding window, where anonymity is focused by different processing rules. The linear search sliding window has a constraint of search overhead and loss of generality under distributed information. In this paper, a new adaptive window approach for privacy coding in data stream mining is proposed. This presented approach is developed with the concern of minimize the search overhead and accuracy in search mining performance using adaptive window monitoring

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The privacy preserving method for dynamic trajectory releasing based on adaptive clustering

Cited by 42 publications

References 10 publications

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

Two privacy-preserving approaches for data publishing with identity reservation

An Adaptive Slide Window Security Method for Transaction Updation in Data Stream Mining

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