On Location Privacy in Fingerprinting-based Indoor Positioning System

Wang, Wenlu; Gong, Zhitao; Zhang, Ji; Lu, Hua; Ku, Wei‐Shinn

doi:10.1145/3347146.3359081

Cited by 2 publications

(3 citation statements)

References 23 publications

(34 reference statements)

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“…Some laws have also been issued by European countries. Presently, there are several ways to preserve privacy when capturing people's moving trajectories in indoor space, e.g., by using an encryption approach [26] or a privacy-preserving protocol [27]. Generally, most privacy-preserving approaches designed for outdoor-space-based location services can be slightly modified to suit indoor-space applications.…”

Section: Discussionmentioning

confidence: 99%

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Cui

2020

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The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, we study the detection of highly influential positions from indoor position-tracking data, e.g., to detect highly influential positions in a business center, or to detect the hottest shops in a shopping mall according to users’ indoor position-tracking data. We first describe three baseline solutions to this problem, which are count-based, density-based, and duration-based algorithms. Then, motivated by the H-index for evaluating the influence of an author or a journal in academia, we propose a new algorithm called H-Count, which evaluates the influence of an indoor position similarly to the H-index. We further present an improvement of the H-Count by taking a filtering step to remove unqualified position-tracking records. This is based on the observation that many visits to a position such as a gate are meaningless for the detection of influential indoor positions. Finally, we simulate 100 moving objects in a real building deployed with 94 RFID readers over 30 days to generate 223,564 indoor moving trajectories, and conduct experiments to compare our proposed H-Count and H-Count* with three baseline algorithms. The results show that H-Count outperforms all baselines and H-Count* can further improve the F-measure of the H-Count by 113% on average.

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Section: Discussionmentioning

confidence: 99%

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Cui

2020

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“…We have found 15 studies [42][43][44][45][46][47][48][49][50][51][73][74][75][76][77] in the literature on privacy-preserving Wi-Fi-based indoor localization published between 2018 and 2022. In Table 4, related papers are classified according to their publication year, used technologies, algorithms, signal properties and methods for providing localization privacy.…”

Section: Evaluation Of Existing Privacy-preserving Wireless Indoor Lo...mentioning

confidence: 99%

“…To maintain anonymity for localization, The Encrypted Indoor Positioning Service (EIPS) approach proposed by Wang W. et al [44] protects user privacy from a centralized server while maintaining localization accuracy. Their EIPS approach allows customers to encrypt and decrypt their requests bi-directionally utilizing an EDS (Encryption and Decryption Server) in a commutative manner, ensuring that both EIPS and EDS remain anonymous.…”

Section: Evaluation Of Existing Privacy-preserving Wireless Indoor Lo...mentioning

confidence: 99%

Privacy-Preserving Wireless Indoor Localization Systems

ADANUR DEDETURK,

KOLUKİSA,

TONYALI

2023

Kocaeli Journal of Science and Engineering

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Recently the number of buildings and interior spaces has increased, and many systems have been proposed to locate people or objects in these environments. At present, several technologies, such as GPS, Bluetooth, Wi-Fi, Ultrasound, and RFID, are used for positioning problems. Some of these technologies provide good results for positioning outdoors whereas some others are effective for indoor environments. While GPS is used for outdoor localization systems, Wi-Fi, Bluetooth, Ultra WideBand, and RFID are used for indoor localization systems (ILSs). Today, due to the proliferation and extensive usage of Wi-Fi access points, wireless-based technologies in indoor localization are preferred more than others. However, even though the abovementioned technologies make life easier for their users, ILSs can pose some privacy risks in case the confidentiality of the location data cannot be ensured. Such an incident is highly likely to result in the disclosure of users’ identity and behavior patterns. In this paper, we aim to investigate existing privacy-preserving wireless ILSs and discuss them.

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On Location Privacy in Fingerprinting-based Indoor Positioning System

Cited by 2 publications

References 23 publications

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Privacy-Preserving Wireless Indoor Localization Systems

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