Is Geo-Indistinguishability What You Are Looking for?

Oya, Simon; Troncoso, Carmela; Pérez-González, Fernando

doi:10.1145/3139550.3139555

Cited by 29 publications

(18 citation statements)

References 10 publications

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“…In this manner, the actual location of the user will never be sent to the LBS. [30] reported that for maintaining a high level of privacy in GeoInd the utility must be sacrificed. Thus, the information retrieved from LBS using GeoInd may not be useful unless users sacrifice some degree of privacy.…”

Section: Privacy Layermentioning

confidence: 99%

A multi-layered blockchain framework for smart mobility data-markets

López

Farooq

2020

Transportation Research Part C: Emerging Technologies

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Blockchain has the potential to render the transaction of information more secure and transparent. Nowadays, transportation data are shared across multiple entities using heterogeneous mediums, from paper collected data to smartphone. Most of this data are stored in central servers that are susceptible to hacks. In some cases shady actors who may have access to such sources, share the mobility data with unwanted third parties. A multi-layered Blockchain framework for Smart Mobility Data-market (BSMD) is presented for addressing the associated privacy, security, management, and scalability challenges. Each participant shares their encrypted data to the blockchain network and can transact information with other participants as long as both parties agree to the transaction rules issued by the owner of the data. Data ownership, transparency, auditability and access control are the core principles of the proposed blockchain for smart mobility data-market. In a case study of real-time mobility data sharing, we demonstrate the performance of BSMD on a 370 nodes blockchain running on heterogeneous and geographically-separated devices communicating on a physical network. We also demonstrate how BSMD ensures the cybersecurity and privacy of individual by safeguarding against spoofing and message interception attacks and providing information access management control. Service 1 that "...(Waze will) share personal information with companies or organizations connected or affiliated with Waze...". So all the information collected by Waze could be shared with other organizations associated with Waze. However, the Terms of Service do not make it clear if the user can track with whom their information has been shared.Another point related to privacy is the access control of the information that is provided by the users. It was recently discovered that Google keeps collecting user location data even if they explicitly deactivate the tracking system in their mobiles [26]. Another example of disclosing information without the user's consent is the infamous Facebook-Cambridge Analytica scandal [9].The General Data Protection Regulation 2 (GDPR) is a step forward for privacy. Although the GDPR is valid only in the European Union, it is still expected to push multinational companies to be more transparent on how they manage people's private information. In the authors opinion, no matter how many rules and fines governments apply, given the current centralized ways of collecting people's data, some entities will always find a loophole in legislation or hackers will be able to tamper with these centralized data systems. In addition to laws, individuals have to have total control and ownership of their information. We need to be the guardians and responsible of our own privacy, unfortunately nowadays we have to trust third parties for that.Distributed ledger technologies like blockchain have the potential to give the people full control of their information, protect individual's personal mobility information and guard their privacy. The ...

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Section: Privacy Layermentioning

confidence: 99%

A multi-layered blockchain framework for smart mobility data-markets

López

Farooq

2020

Transportation Research Part C: Emerging Technologies

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“…Differential privacy is guaranteed by adding noise, drawn from a two-dimensional Laplace distribution. Further version of Geo-I have been developed in [5] and [22].…”

Section: A Location Privacy Protection Mechanismsmentioning

confidence: 99%

Automatic Privacy and Utility Preservation for Mobility Data: A Nonlinear Model-Based Approach

Cerf

Bouchenak

Robu

et al. 2021

IEEE Trans. Dependable and Secure Comput.

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The widespread use of mobile devices and locationbased services has generated massive amounts of mobility databases. While processing these data is highly valuable, privacy issues can occur if personal information is revealed. The prior art has investigated ways to protect mobility data by providing a large range of Location Privacy Protection Mechanisms (LPPMs). However, the privacy level of the protected data significantly varies depending on the protection mechanism used, its configuration and on the characteristics of the mobility data. Meanwhile, the protected data still needs to enable some useful processing. To tackle these issues, we present PULP, a framework that finds the suitable protection mechanism and automatically configures it for each user in order to achieve user-defined objectives in terms of both privacy and utility. PULP uses nonlinear models to capture the impact of each LPPM on data privacy and utility levels. Evaluation of our framework is carried out with two protection mechanisms of the literature and four realworld mobility datasets. Results show the efficiency of PULP, its robustness and adaptability. Comparisons between LPPMs' configurator and the state of the art further illustrate that PULP better realizes users' objectives and its computations time is in orders of magnitude faster.

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“…Those works both introduce a model and a number of metrics to evaluate privacy and utility of LPPMs. Indeed, despite formal guarantees are needed in most contexts, they do not always translate well how an LPPM behaves in practice, as it has been shown in several works (e.g., [156], [157], [158]). On the privacy side, we advocate (similarly to, e.g., [124]) that metrics relying on adversary attacks should be considered as complimentary to the formal guarantees.…”

Section: A Quantifying Location Privacymentioning

confidence: 99%

The Long Road to Computational Location Privacy: A Survey

Primault

Boutet

Mokhtar

et al. 2019

IEEE Commun. Surv. Tutorials

137

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The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting directions to work in the morning, leaving a check-in at a restaurant at noon and checking next day's weather in the evening are possible right from any mobile device embedding a GPS chip. In these location-based applications, the user's location is sent to a server, which uses them to provide contextual and personalised answers. However, nothing prevents the latter from gathering, analysing and possibly sharing the collected information, which opens the door to many privacy threats. Indeed, mobility data can reveal sensitive information about users, among which one's home, work place or even religious and political preferences. For this reason, many privacy-preserving mechanisms have been proposed these last years to enhance location privacy while using geolocated services. This article surveys and organises contributions in this area from classical building blocks to the most recent developments of privacy threats and location privacy-preserving mechanisms. We divide the protection mechanisms between online and offline use cases, and organise them into six categories depending on the nature of their algorithm. Moreover, this article surveys the evaluation metrics used to assess protection mechanisms in terms of privacy, utility and performance. Finally, open challenges and new directions to address the problem of computational location privacy are pointed out and discussed.

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Is Geo-Indistinguishability What You Are Looking for?

Cited by 29 publications

References 10 publications

A multi-layered blockchain framework for smart mobility data-markets

A multi-layered blockchain framework for smart mobility data-markets

Automatic Privacy and Utility Preservation for Mobility Data: A Nonlinear Model-Based Approach

The Long Road to Computational Location Privacy: A Survey

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