Location Anonymization With Considering Errors and Existence Probability

Sei, Yuichi; Ohsuga, Akihiko

doi:10.1109/tsmc.2016.2564928

Cited by 9 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The setup includes 10,000 users interacting in a space measuring 8.4 km x 8.4 km, which includes businesses, restaurants, and parks. We used the data for this simulation based on previous research by Sei et al [29].…”

Section: Simulation Methodsmentioning

confidence: 99%

Minimizing Noise in Location Privacy Protection Through Equipment Error Consideration

Isawa,

Sei,

Tahara

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

In recent years, systems that collect location information and publish statistics, such as those that publish congestion information, have been extensively employed. Because it is possible to infer an individual’s identity even if the information is not directly disclosed, it is essential to disclose data with privacy protection. Therefore, privacy protection methods based on differential privacy are attracting attention. Geo-indistinguishability is the most famous extension theorem of differential privacy for location information. Geo-indistinguishability can be achieved by adding noise to a target value that must be protected. However, noise addition reduces the usefulness of the data. Thus, it is desirable to add minimal noise to your privacy budget. Therefore, we focus on the fact that the values obtained using measurement devices contain errors. We introduced a novel concept of differential privacy tailored for location information, termed true-value-based geo-indistinguishability (T-Geo-I), which accounts for equipment noise. We also proposed a location information privacy protection method that considers T-Geo-I and reduces the amount of added noise. The object of privacy protection should be the “true value” not the “measured value” that includes measurement errors. According to the experimental results, in the case wherein the measurement error is the normal distribution, our method reduced the noise average and mean square error (MSE) by up to 41% and 63%, respectively, compared with conventional methods while maintaining a prespecified level of privacy in 108 samples of numerical data. In the case wherein the measurement error is the lognormal distribution, the proposed method based on T-Geo-I succeeded in reducing the noise average and MSE by up to 60% and 67%, respectively, compared with methods based on Geo-I, while maintaining a prespecified level of privacy. These findings indicate that the proposed method can improve the usefulness of data while maintaining a prespecified degree of privacy protection.

show abstract

Section: Simulation Methodsmentioning

confidence: 99%

Minimizing Noise in Location Privacy Protection Through Equipment Error Consideration

Isawa,

Sei,

Tahara

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

show abstract

“…In contrast, the proposed scenario seeks to obtain each person's value as accurately as possible, as services like recommender systems require individual attribute values. Abul et al [ 2] and Sei et al [ 53] put forth location anonymization methods that consider location error and achieve.k-anonymity [ 41,42,58,65], which is a fundamental privacy metric. However, these methods are not applicable to .∈-differential privacy.…”

Section: Related Research Workmentioning

confidence: 99%

Privacy-Preserving Data Collection and Analysis for Smart Cities

Sei

2024

Human-Centered Services Computing for Smart Cities

Self Cite

View full text Add to dashboard Cite

Smart cities leverage real-world data to digitally replicate city-related aspects such as disaster prevention, transportation, and pandemics, creating an encompassing virtual environment. The construction of this realistic virtual world necessitates the collection of individual behavioral data through Internet of Things (IoT) environments. However, the challenge lies in ensuring the privacy of individuals during this data collection process. While numerous studies exist on privacypreserving data mining, most target clean, complete, and independent personal data. This overlooks the reality of real-world personal data, which often contains noise, missing values, and evidence of interpersonal interactions. To build a human-centric smart city, it is crucial to consider these imperfect and interactive data while preserving privacy. In this paper, we propose a novel framework for privacy-preserving data collection and analysis in smart cities. This framework acknowledges the inherent sensing errors and interpersonal interactions, ensuring a more accurate representation of real-world conditions while maintaining stringent privacy safeguards. IntroductionComprehensive efforts have been made to establish an intelligent urban environment by developing sophisticated digital twins. Digital twins encapsulate the diverse functionalities of urban landscapes and accurately represent the behavioral patterns of their inhabitants. By creating a virtual counterpart to a city from data gathered in the real world, a myriad of urban-related events and processes, including disaster mitigation, transportation management, and pandemic response, are simulated within the digital realm. Subsequently, the insights gleaned from these simulations can be fed back into the physical world to facilitate well-informed decision-making and foster more sustainable urban development.

show abstract

“…(1) In pseudonym methods, the user identity in each LBS query is replaced with a pseudonym, so as to disconnect the user from the query [11], [21]. However, for this kind of methods, although such solutions as establishing mixed zones have been designed to improve the effectiveness of pseudonyms [18], [19], [20], [2], it is still likely for an attacker to identify the user identity from query content itself, due to no change to each user query, i.e., it is difficult to resist the threat from data mining [9], [31]. For example, in [10], a novel paradigm of de-anonymization attacks based on mobility patterns of objects was proposed, which can re-identify the user trajectory accurately from a group of anonymous trajectories (where the real identities of mobile objects has been replaced with pseudonyms).…”

Section: Related Workmentioning

confidence: 99%

A Location Privacy-Preserving System Based on Query Range Cover-Up or Location-Based Services

Wang

et al. 2020

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Location-based service (LBS) has been widely used in various fields of industry, and become a vital part of people's daily life. However, while providing great convenience for users, LBS results in a serious threat on users' location privacy, due to its more and more untrusted server-side. In this paper, we propose a location privacy-preserving system for LBS by constructing "cover-up ranges" to protect the query ranges associated with a location query sequence. Firstly, we present a clientbased system framework for location privacy protection in LBS, which requires no compromise to the accuracy and usability of LBS. Secondly, based on the framework, we introduce a location privacy model to formulate the constraints that ideal cover-up ranges should satisfy, so as to improve the efficiency of location services and the security of location privacy. Finally, we describe an implementation algorithm to well meet the location privacy model. Both theoretical analysis and experimental evaluation demonstrate the effectiveness of our system, which can improve the security of users' location privacy on the untrusted serverside, without compromising the accuracy and usability of LBS.

show abstract

Location Anonymization With Considering Errors and Existence Probability

Cited by 9 publications

References 33 publications

Minimizing Noise in Location Privacy Protection Through Equipment Error Consideration

Minimizing Noise in Location Privacy Protection Through Equipment Error Consideration

Privacy-Preserving Data Collection and Analysis for Smart Cities

A Location Privacy-Preserving System Based on Query Range Cover-Up or Location-Based Services

Contact Info

Product

Resources

About