It's All About 'Location, Location, Location' - Privacy Concerns and the RFID Debate

2009 ◽  
Author(s):  
Adrian Patrick Bannon
Keyword(s):  
Location Privacy ◽  
Privacy Concerns

scholarly journals Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

scholarly journals SSID Oracle Attack on Undisclosed Wi-Fi Preferred Network Lists

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ante Dagelić ◽  
Toni Perković ◽  
Bojan Vujatović ◽  
Mario Čagalj
Keyword(s):  
Location Privacy ◽  
Real Life ◽  
User Mobility ◽  
Window Of Opportunity ◽  
Privacy Concerns ◽  
Private Location ◽  
Life Tests ◽  
Active Attacks ◽  
Recommender Algorithm ◽  
Dictionary Attacks

User’s location privacy concerns have been further raised by today’s Wi-Fi technology omnipresence. Preferred Network Lists (PNLs) are a particularly interesting source of private location information, as devices are storing a list of previously used hotspots. Privacy implications of a disclosed PNL have been covered by numerous papers, mostly focusing on passive monitoring attacks. Nowadays, however, more and more devices no longer transmit their PNL in clear, thus mitigating passive attacks. Hidden PNLs are still vulnerable against active attacks whereby an attacker mounts a fake SSID hotspot set to one likely contained within targeted PNL. If the targeted device has this SSID in the corresponding PNL, it will automatically initiate a connection with the fake hotspot thus disclosing this information to the attacker. By iterating through different SSIDs (from a predefined dictionary) the attacker can eventually reveal a big part of the hidden PNL. Considering user mobility, executing active attacks usually has to be done within a short opportunity window, while targeting nontrivial SSIDs from user’s PNL. The existing work on active attacks against hidden PNLs often neglects both of these challenges. In this paper we propose a simple mathematical model for analyzing active SSID dictionary attacks, allowing us to optimize the effectiveness of the attack under the above constraints (limited window of opportunity and targeting nontrivial SSIDs). Additionally, we showcase an example method for building an effective SSID dictionary using top-N recommender algorithm and validate our model through simulations and extensive real-life tests.

scholarly journals Constructing elastic distinguishability metrics for location privacy

2015 ◽  
Vol 2015 (2) ◽  
pp. 156-170 ◽  
Author(s):  
Konstantinos Chatzikokolakis ◽  
Catuscia Palamidessi ◽  
Marco Stronati
Keyword(s):  
Location Privacy ◽  
Differential Privacy ◽  
City Center ◽  
Privacy Concerns ◽  
Extensive Evaluation ◽  
Elastic Mechanism ◽  
Real World Datasets ◽  
Location Based Social Networks ◽  
The City ◽  
Geometrical Distance

Abstract With the increasing popularity of hand-held devices, location-based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their users. The recently introduced notion of geo-indistinguishability tries to address this problem by adapting the well-known concept of differential privacy to the area of location-based systems. Although geo-indistinguishability presents various appealing aspects, it has the problem of treating space in a uniform way, imposing the addition of the same amount of noise everywhere on the map. In this paper we propose a novel elastic distinguishability metric that warps the geometrical distance, capturing the different degrees of density of each area. As a consequence, the obtained mechanism adapts the level of noise while achieving the same degree of privacy everywhere. We also show how such an elastic metric can easily incorporate the concept of a “geographic fence” that is commonly employed to protect the highly recurrent locations of a user, such as his home or work. We perform an extensive evaluation of our technique by building an elastic metric for Paris’ wide metropolitan area, using semantic information from the OpenStreetMap database. We compare the resulting mechanism against the Planar Laplace mechanism satisfying standard geo-indistinguishability, using two real-world datasets from the Gowalla and Brightkite location-based social networks. The results show that the elastic mechanism adapts well to the semantics of each area, adjusting the noise as we move outside the city center, hence offering better overall privacy.1

scholarly journals Toward Addressing Location Privacy Issues: New Affiliations with Social and Location Attributes

2019 ◽  
Vol 11 (11) ◽  
pp. 234 ◽  
Author(s):  
Vgena ◽  
Kitsiou ◽  
Kalloniatis ◽  
Kavroudakis ◽  
Gritzalis
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Information Disclosure ◽  
Location Privacy ◽  
Location Information ◽  
Sensitive Information ◽  
Privacy Concerns ◽  
Location Sharing ◽  
Privacy Issues ◽  
Different Levels

Nowadays, location-sharing applications (LSA) within social media enable users to share their location information at different levels of precision. Users on their side are willing to disclose this kind of information in order to represent themselves in a socially acceptable online way. However, they express privacy concerns regarding potential malware location-sharing applications, since users’ geolocation information can provide affiliations with their social identity attributes that enable the specification of their behavioral normativity, leading to sensitive information disclosure and privacy leaks. This paper, after a systematic review on previous social and privacy location research, explores the overlapping of these fields in identifying users’ social attributes through examining location attributes while online, and proposes a targeted set of location privacy attributes related to users’ socio-spatial characteristics within social media.

Geosurveillance, Location Privacy, and Personalization

2019 ◽  
Vol 38 (4) ◽  
pp. 484-499 ◽  
Author(s):  
Syagnik Banerjee
Keyword(s):  
Data Storage ◽  
Location Privacy ◽  
Data Service ◽  
Policy Makers ◽  
Privacy Concerns ◽  
Digital Footprint ◽  
Location Data ◽  
Legal Standards ◽  
Risk Profiling ◽  
The Rich

As connected consumers expand their digital footprint, firms are legally purchasing location data generated by apps, sold to intermediaries, and cleaned by analytics vendors for personalized targeting, advertising, and risk profiling. Data storage and flow across multiple sectors and states cause increased variability in agency jurisdiction, legal standards, and premise for legal recourse to privacy violations. To better inform industries, policy makers, and consumers in this rapidly changing environment, the author develops a new construct, location privacy, articulating the rich impact of geosurveillance on the consumer. Analysis of studies conducted using car GPS and wearable devices find that data service provider familiarity (known, unknown) and georeferencing style (environment, movement) affect location privacy concerns and the adoption likelihood of personalized driving and health insurance policies underwritten with disclosed location data. The article discusses implications about potential marketer liabilities and regulators’ roles in moderating the market’s concerns regarding geosurveillance.

scholarly journals PrivateRide: A Privacy-Enhanced Ride-Hailing Service

2017 ◽  
Vol 2017 (2) ◽  
pp. 38-56 ◽  
Author(s):  
Anh Pham ◽  
Italo Dacosta ◽  
Bastien Jacot-Guillarmod ◽  
Kévin Huguenin ◽  
Taha Hajar ◽  
...  
Keyword(s):  
High Risk ◽  
Location Privacy ◽  
Real Data ◽  
Data Sets ◽  
Mobility Patterns ◽  
Transportation Services ◽  
Privacy Concerns ◽  
Privacy Threats ◽  
The Past ◽  
Online Marketplace

AbstractIn the past few years, we have witnessed a rise in the popularity of ride-hailing services (RHSs), an online marketplace that enables accredited drivers to use their own cars to drive ride-hailing users. Unlike other transportation services, RHSs raise significant privacy concerns, as providers are able to track the precise mobility patterns of millions of riders worldwide. We present the first survey and analysis of the privacy threats in RHSs. Our analysis exposes high-risk privacy threats that do not occur in conventional taxi services. Therefore, we propose PrivateRide, a privacy-enhancing and practical solution that offers anonymity and location privacy for riders, and protects drivers’ information from harvesting attacks. PrivateRide lowers the high-risk privacy threats in RHSs to a level that is at least as low as that of many taxi services. Using real data-sets from Uber and taxi rides, we show that PrivateRide significantly enhances riders’ privacy, while preserving tangible accuracy in ride matching and fare calculation, with only negligible effects on convenience. Moreover, by using our Android implementation for experimental evaluations, we show that PrivateRide’s overhead during ride setup is negligible. In short, we enable privacy-conscious riders to achieve levels of privacy that are not possible in current RHSs and even in some conventional taxi services, thereby offering a potential business differentiator.

scholarly journals Location Based Services for Sharing Using Trusted Server

2020 ◽  
pp. 68-73
Keyword(s):  
Social Networks ◽  
Location Privacy ◽  
Location Based Services ◽  
Location Information ◽  
Location Based Service ◽  
Privacy Concerns ◽  
Computational Overhead ◽  
Location Sharing ◽  
Current Location ◽  
Cryptographic Primitive

The main aim of location-sharing is to provide current location information to their designated users. Nowadays, Location Based Service (LBS) has become one of the popular services which are provided by social networks. As LBS activity makes use of the user's identity and current location information, an appropriate path has to be utilized to protect the location privacy. However, as per our knowledge, there is no access to protecting the location sharing with the complete privacy of the location. To consider this issue, we put forward a new cryptographic primitive functional pseudonym for location sharing that make sure privacy of the data. Also, the proposed approach notably reduces the computational overhead of users by delegating part of the computation for location sharing to a server, therefore it is endurable. The primitive can be widely used in many MOSNs to authorize LBS with enhanced privacy and sustainability. As a result, it will contribute to proliferate LBS by eliminating user's privacy concerns.

Bidirectional Random Walk Based Mobile-Sink-LocationPrivacyin WSN

2014 ◽  
Vol 529 ◽  
pp. 730-734
Author(s):  
Jun Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Random Walk ◽  
Sensor Networks ◽  
Data Collection ◽  
Location Privacy ◽  
Mobile Sink ◽  
Wireless Sensor ◽  
Random Data ◽  
Privacy Concerns ◽  
Sink Location

As wide applications of wireless sensor networks, privacy concerns have emerged as the main obstacle to success. When wireless sensor networks are used to battlefield, the privacy about sink-locations become a crux issue. If sink location will be exposed to adversary, the consequence is inconceivable. Random data collection scheme has a problem that message latencies become larger higher for protecting mobile-sink-locationprivacy .In this paper, BDRW (Bidirectional Random Walk) is proposed to preserve mobile-sink-location privacy. In BDRW, data are forwarded by directional random walk and stored at pass nodes in the network, the sink move in directional random walk to collect data from the local nodes occasionally, which prevents the attackers from predicting their locations and movements. Compared to random data collection scheme, BDRW has smaller message latencies, while providing satisfactory mobile-sink-location privacy.

A Privacy Preserving Scheme for Vehicle to Grid Networks Based on Homomorphic Cryptography

2014 ◽  
Vol 1014 ◽  
pp. 516-519
Author(s):  
Zhong Wei Sun ◽  
Wen Xiao Yan
Keyword(s):  
Location Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Close Monitoring ◽  
Fully Homomorphic Encryption ◽  
Privacy Concerns ◽  
Vehicle To Grid ◽  
Grid Networks ◽  
The Status ◽  
Communication Scheme

Vehicle–to-Grid (V2G) is an essential component of smart grid for their capability of providing better ancillary services. The operation is based on monitoring the status of individual Electric Vehicle (EV) continuously and designing an incentive scheme to attract sufficient participating EVs. However, the close monitoring might raise privacy concerns from the EV owners about real identity and location leakage. Based on the fully homomorphic encryption algorithm, a privacy preserving V2G communication scheme is put forward in the paper. The proposed protocol can achieve the identity and location privacy, security requirement of confidentiality and integrity of the communications.

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