Location Privacy using Homomorphic Encryption over Cloud

Rohilla, Alisha; Khurana, Mehak; Singh, Latika

doi:10.5815/ijcnis.2017.08.05

Cited by 12 publications

(8 citation statements)

References 6 publications

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“…Homomorphic Encryption As has been explained in previous chapters the advantage of homomorphic encryption is that data can be computed while it is encrypted, guaranteeing that computations on the data lead to the same results on the decrypted data. Rohilla et al [52] provide a prototype, showing how location privacy using homomorphic encryption over the cloud cold be realized. But distinctly from our use case, they assume only one person requesting a location-based service.…”

Section: Randomizationmentioning

confidence: 99%

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Rannenberg¹,

Pape²,

Tronnier³

et al. 2021

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The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data. For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.

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Section: Randomizationmentioning

confidence: 99%

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Rannenberg¹,

Pape²,

Tronnier³

et al. 2021

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show abstract

“…e server answers queries based on the transformed data other than the original data. Some approaches use cryptography [22][23][24] like homomorphic encryption [8,14] to encrypt the data of the users and enable queries on encrypted data. ese approaches avoid transmitting the original user data during the process of the query, thus protecting the user privacy, but they do not consider the privacy leakage from the query results, which motivated the attack proposed in this paper.…”

Section: 2mentioning

confidence: 99%

Limiting Privacy Breaches in Average-Distance Query

Xia

Xiong

Huang

et al. 2020

Security and Communication Networks

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Querying average distances is useful for real-world applications such as business decision and medical diagnosis, as it can help a decision maker to better understand the users’ data in a database. However, privacy has been an increasing concern. People are now suffering serious privacy leakage from various kinds of sources, especially service providers who provide insufficient protection on user’s private data. In this paper, we discover a new type of attack in an average-distance query (AVGD query) with noisy results. The attack is general that it can be used to reveal private data of different dimensions. We theoretically analyze how different factors affect the accuracy of the attack and propose the privacy-preserving mechanism based on the analysis. We experiment on two real-life datasets to show the feasibility and severity of the attack. The results show that the severity of the attack is mainly influenced by the factors including the noise magnitude, the number of queries, and the number of users in each query. Also, we validate the correctness of our theoretical analysis by comparing with the experimental results and confirm the effectiveness of the privacy-preserving mechanism.

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“…In this protocol, we use a fully homomorphic encryption (FHE) [88]. This technique is detailed in [89,90]. We repeat all the steps (excluding the last one) from the protocol of vehicle-side distance calculation, shifting this calculation to the side of the base station.…”

Section: Location Determination With Mutual Base Station Authenticmentioning

confidence: 99%

Positioning Information Privacy in Intelligent Transportation Systems: An Overview and Future Perspective

Ometov

Bezzateev

Давыдов

et al. 2019

Sensors

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Today, the Intelligent Transportation Systems (ITS) are already in deep integration phase all over the world. One of the most significant enablers for ITS are vehicle positioning and tracking techniques. Worldwide integration of ITS employing Dedicated Short Range Communications (DSRC) and European standard for vehicular communication, known as ETSI ITS-G5, brings a variety of options to improve the positioning in areas where GPS connectivity is lacking precision. Utilization of the ready infrastructure, next-generation cellular 5G networks, and surrounding electronic devices together with conventional positioning techniques could become the solution to improve the overall ITS operation in vehicle-to-everything (V2X) communication scenario. Nonetheless, effective and secure communication protocols between the vehicle and roadside units should be both analyzed and improved in terms of potential attacks on the transmitted positioning-related data. In particular, said information might be misused or stolen at the infrastructure side conventionally assumed to be trusted. In this paper, we first survey different methods of vehicle positioning, which is followed by an overview of potential attacks on ITS systems. Next, we propose potential improvements allowing mutual authentication between the vehicle and infrastructure aiming at improving positioning data privacy. Finally, we propose a vision on the development and standardization aspects of such systems.

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Location Privacy using Homomorphic Encryption over Cloud

Cited by 12 publications

References 6 publications

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Limiting Privacy Breaches in Average-Distance Query

Positioning Information Privacy in Intelligent Transportation Systems: An Overview and Future Perspective

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