Location recommendation privacy protection method based on location sensitivity division

Yin, Chunyong; Ju, Xiaokang; Yin, Zhichao; Wang, Jin

doi:10.1186/s13638-019-1606-y

Cited by 18 publications

(10 citation statements)

References 31 publications

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“…However, as the tree grows, the prefix will form a large number of leaf nodes, resulting in too much noise and reducing the accuracy of the published data set. Later, on the basis of prefix method, location trajectory and check-in frequency are used to set thresholds in [ 18 ], so as to classify the level of location sensitivity. Then, the corresponding privacy budget is allocated according to the sensitivity, which makes the allocation of privacy budget more reasonable and reduces the amount of noise data.…”

Section: Related Workmentioning

confidence: 99%

“…The location information to be protected is the trajectory data published by the mobile smart device, including the user check-in time, location identification (ID), longitude and latitude. These assumptions are used in most previous works, such as [ 7 , 17 , 18 ]. In addition, the SPDP scheme proposed in this paper uses differential privacy technology, prefix tree structure, Markov chain and so on to protect the trajectory data.…”

Section: Preliminariesmentioning

confidence: 99%

“…In order to prove the effectiveness of SPDP in this paper, it is compared with BOSD in reference [ 18 ] and UD of uniform distribution in reference [ 17 ]. In this experiment, a total of three levels of location privacy are set.…”

Section: Performance Evaluationsmentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Preliminariesmentioning

confidence: 99%

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Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

Zhang

Ding

et al. 2022

Entropy

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“…With the continuous development of information technology, from the security of state secrets to the security of personal privacy, the issue of information security is increasingly concerned by the society and researchers [1][2][3][4][5][6][7][8][9]. Random numbers (RNs) are closely related to cryptography, which has attracted much attention due to its extreme sensitivity to keys, mixed data, pseudorandom behavior, and determinism.…”

Section: Introductionmentioning

confidence: 99%

Pseudorandom Number Generator Based on Three Kinds of Four-Wing Memristive Hyperchaotic System and Its Application in Image Encryption

Chen

Qian

et al. 2020

Complexity

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In this paper, we propose a method to design the pseudorandom number generator (PRNG) using three kinds of four-wing memristive hyperchaotic systems (FWMHSs) with different dimensions as multientropy sources. The principle of this method is to obtain pseudorandom numbers with good randomness by coupling XOR operation on the three kinds of FWMHSs with different dimensions. In order to prove its potential application in secure communication, the security of PRNG based on this scheme is analyzed from the perspective of cryptography. In addition, PRNG has passed the NIST 800.22 and ENT test, which shows that PRNG has good statistical characteristics. Finally, an image encryption algorithm based on PRNG is adopted. In the encryption algorithm, the optimized Arnold matrix scrambling method and the diffusion processing based on XOR are used to obtain the final encrypted image. Through the evaluation of encryption performance, it is concluded that there is no direct relationship between the pristine image and encrypted image. The results show that the proposed image encryption scheme has good statistical output characteristics and security performance in line with cryptography.

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“…It is one of the biggest obstacles to use more accurate personal location information. Many research studies have been carried out to solve this privacy violation [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], and differential privacy, which is accepted as a de facto standard among the privacy protection techniques, is being studied to protect the privacy of personal location data.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Grid and Incentive Mechanism for Personalized Differentially Private Location Data in the Local Setting

Jung

Park

2020

Mobile Information Systems

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With the proliferation of wireless communication and mobile devices, various location-based services are emerging. For the growth of the location-based services, more accurate and various types of personal location data are required. However, concerns about privacy violations are a significant obstacle to obtain personal location data. In this paper, we propose a local differential privacy scheme in an environment where there is no trusted third party to implement privacy protection techniques and incentive mechanisms to motivate users to provide more accurate location data. The proposed local differential privacy scheme allows a user to set a personalized safe region that he/she can disclose and then perturb the user’s location within the safe region. It is the way to satisfy the user’s various privacy requirements and improve data utility. The proposed incentive mechanism has two models, and both models pay the incentive differently according to the user’s safe region size to motivate to set a more precise safe region. We verify the proposed local differential privacy algorithm and incentive mechanism can satisfy the privacy protection level while achieving the desirable utility through the experiment.

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Location recommendation privacy protection method based on location sensitivity division

Cited by 18 publications

References 31 publications

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

Pseudorandom Number Generator Based on Three Kinds of Four-Wing Memristive Hyperchaotic System and Its Application in Image Encryption

An Adaptive Grid and Incentive Mechanism for Personalized Differentially Private Location Data in the Local Setting

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