Hide me Behind the Noise: Local Differential Privacy for Indoor Location Privacy

Navidan, Hojjat; Moghtadaiee, Vahideh; Nazaran, Niki; Alishahi, Mina

doi:10.1109/eurospw55150.2022.00061

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…Many other LDP methods have been proposed. Navidan et al proposed a framework that estimates the number of people in each area while protecting each user's location privacy using LDP [ 40]. In this framework, users measure the Received Signal Strength Indicator (RSSI) and determine their locations based on the RSSI.…”

Section: Related Work On Ldpmentioning

confidence: 99%

Privacy-Preserving Data Collection and Analysis for Smart Cities

Sei

2024

Human-Centered Services Computing for Smart Cities

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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.

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Section: Related Work On Ldpmentioning

confidence: 99%

Privacy-Preserving Data Collection and Analysis for Smart Cities

Sei

2024

Human-Centered Services Computing for Smart Cities

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“…They used the circle-based dummy generation (CBDG) algorithm to create some dummy locations before sending the query to a trusted third party. The local differential privacy technique is used in [ 28 ] to present a novel privacy-aware framework for aggregating indoor location data. In this technique, user location data are altered locally in the user’s device and then transferred to the aggregator.…”

Section: Related Workmentioning

confidence: 99%

Location Privacy-Preserving Scheme in IoBT Networks Using Deception-Based Techniques

Alkanjr

Mahgoub

2023

Sensors

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The Internet of Battlefield Things (IoBT) refers to interconnected battlefield equipment/sources for synchronized automated decision making. Due to difficulties unique to the battlefield, such as a lack of infrastructure, the heterogeneity of equipment, and attacks, IoBT networks differ significantly from regular IoT networks. In war scenarios, real-time location information gathering is critical for combat effectiveness and is dependent on network connectivity and information sharing in the presence of an enemy. To maintain connectivity and guarantee the safety of soldiers/equipment, location information must be exchanged. The location, identification, and trajectory of soldiers/devices are all contained in these messages. A malicious attacker may utilize this information to build a complete trajectory of a target node and track it. This paper proposes a location privacy-preserving scheme in IoBT networks using deception-based techniques. Dummy identifier (DID), sensitive areas location privacy enhancement, and silence period concepts are used to minimize the attacker’s ability to track a target node. In addition, to consider the security of the location information, another security layer is proposed, which generates a pseudonym location for the source node to use instead of its real location when sending messages in the network. We develop a Matlab simulation to evaluate our scheme in terms of average anonymity and probability of linkability of the source node. The results show that the proposed method improves the anonymity of the source node. It reduces the attacker’s ability to link the old DID of the source node with its new DID. Finally, the results show further privacy enhancement by applying the sensitive area concept, which is important for IoBT networks.

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“…When enriching data, randomly generated noise is usually added to the actual data in order to reduce the accuracy of the data-and thus the amount of information contained [49]. However, such a one-size-fits-all approach is not always effective.…”

Section: Related Workmentioning

confidence: 99%

SMARTEN—A Sample-Based Approach towards Privacy-Friendly Data Refinement

Stach

Behringer

Bräcker

et al. 2022

JCP

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Two factors are crucial for the effective operation of modern-day smart services: Initially, IoT-enabled technologies have to capture and combine huge amounts of data on data subjects. Then, all these data have to be processed exhaustively by means of techniques from the area of big data analytics. With regard to the latter, thorough data refinement in terms of data cleansing and data transformation is the decisive cornerstone. Studies show that data refinement reaches its full potential only by involving domain experts in the process. However, this means that these experts need full insight into the data in order to be able to identify and resolve any issues therein, e.g., by correcting or removing inaccurate, incorrect, or irrelevant data records. In particular for sensitive data (e.g., private data or confidential data), this poses a problem, since these data are thereby disclosed to third parties such as domain experts. To this end, we introduce SMARTEN, a sample-based approach towards privacy-friendly data refinement to smarten up big data analytics and smart services. SMARTEN applies a revised data refinement process that fully involves domain experts in data pre-processing but does not expose any sensitive data to them or any other third-party. To achieve this, domain experts obtain a representative sample of the entire data set that meets all privacy policies and confidentiality guidelines. Based on this sample, domain experts define data cleaning and transformation steps. Subsequently, these steps are converted into executable data refinement rules and applied to the entire data set. Domain experts can request further samples and define further rules until the data quality required for the intended use case is reached. Evaluation results confirm that our approach is effective in terms of both data quality and data privacy.

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Hide me Behind the Noise: Local Differential Privacy for Indoor Location Privacy

Cited by 11 publications

References 22 publications

Privacy-Preserving Data Collection and Analysis for Smart Cities

Privacy-Preserving Data Collection and Analysis for Smart Cities

Location Privacy-Preserving Scheme in IoBT Networks Using Deception-Based Techniques

SMARTEN—A Sample-Based Approach towards Privacy-Friendly Data Refinement

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