Parking recommender system privacy preservation through anonymization and differential privacy

Saleem, Yasir; Rehmani, Mubashir Husain; Crespi, Noël; Minerva, Roberto

doi:10.1002/eng2.12297

Cited by 14 publications

(12 citation statements)

References 36 publications

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“…W.A.Amiri et al [44] [45] proposed a smart parking system using blockchain in which the author used cloaking technique to generalize the cars locations into cloaking area to preserve the privacy of the drivers. Some parking service providers have adopted differential privacy such that Y. Saleem et al [24]. However, these systems have a major drawback that they need a third party for data perturbation and rely on a centralized parking management system or server, are prone to SPOF attacks, and face issues like availability.…”

Section: Related Workmentioning

confidence: 99%

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A Secure and Privacy Preserved Parking Recommender System Using Elliptic Curve Cryptography and Local Differential Privacy

et al. 2022

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The privacy preservation has received considerable attention from organizations as the growing population is apprehensive regarding personal data being preserved. Smart Parking is a parking strategy that combines technology and human innovation in an effort to use as few resources as possible(such as time and space) to achieve faster and easier parking spots of vehicles. Smart parking systems utilize third-party parking recommender systems to offer customized parking space recommendations to its users based on their past parking experience. However, indiscriminately sharing a user's data with a third party recommendation system may expose their personal information. As their activity and node mobility can be deduced from previous paring experience. There are several privacy and security issues in existing systems, such as identity and location disclosure, availability and authenticity issues. Another problem with existing solutions is that most distributed systems need a third party to anonymize user data for privacy preservation. Therefore, this article fills the described research gaps by introducing parking recomender systems using Local Differential Privacy (LDP) and Elliptic Curve Cryptography (ECC). Based on ECC we proposed the mutual authentication mechanism using Hash-based message authentication code (HMAC) to provide anonymity and integrity during communication. Moreover, given the risks to security and privacy posed by untrustworthy third parties. We used LDP which uses the Laplace distribution technique to add noise randomly and eliminates any necessity for a third party for data perturbation. In addition to LDP , we utilized the IOTA distributed ledger technology (DLT) to provide a new level of security that ensures immutability, scalability, and quantum secrecy and decentralized the system. Our experiments demonstrate that, in addition to preserving the driver's privacy and security, our proposed model has low storage overheads, computation, and communication costs.

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Section: Related Workmentioning

confidence: 99%

“…As a result, where there is no trustworthy third-party service provider, it is essential to examine how to guarantee that private information is not exposed. Some parking service providers have adopted differential privacy [24].…”

Section: Introductionmentioning

confidence: 99%

A Secure and Privacy Preserved Parking Recommender System Using Elliptic Curve Cryptography and Local Differential Privacy

et al. 2022

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“…In a similar study, [27] combined two different approaches: k-anonymity and perturbation techniques to preserve the privacy of users' parking information and mobility pattern in a privacy-preserving parking spot recommender system. The anonymization technique was applied on users' parking information, while users' parking spot request is perturbed, hence making the user indistinguishable.…”

Section: B Privacy-preserving Recommendationsmentioning

confidence: 99%

Privacy-Preserving Matrix Factorization for Cross-Domain Recommendation

2021

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Cross-domain recommender systems are known to provide solutions to the cold start and data sparsity problems in recommender systems. This can be achieved by leveraging sufficient ratings and users' profiles in one domain to enhance accurate recommendations in another domain. However, domains with sufficient ratings are not willing to share their users' ratings with other recommender systems or domains due to users' privacy and legal concern. Hence this shows a need for a privacy-preserving mechanism that encourages secure knowledge transfer between different domains. This study proposes a privacy-preserving cross-domain recommender system based on matrix factorization. Specifically, the study formally described the privacy requirements of a cross-domain recommender system, which are different from a single domain recommender system. It designs a new framework for a privacy-preserving cross-domain recommender system and then utilized the somewhat homomorphic encryption (SWHE) scheme to ensure users' privacy. The SWHE scheme was used to encrypt users' ratings in different domains, shared latent factor approach was implemented between the domains and extracted knowledge was securely transferred from the source domain to the target domain. We prove that users' privacy is secured throughout the stages involved in the proposed protocol. Experiments on both synthetic and real datasets demonstrate the efficiency of our protocol.

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“…A novel privacy preservation method based on l-diversity and t-closeness was proposed (Sei et al , 2019) that in turn anonymized database with high data quality in a stipulated time interval. Saleem et al (2020) discussed two solutions that preserve privacy using recommender system using differential privacy technique that in turn evolved the tradeoff between privacy and data utility.…”

Section: Literature Review Of Related Workmentioning

confidence: 99%

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

Sujatha

Udayarani

2021

IJICC

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PurposeThe purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback.Design/methodology/approachIn this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.FindingsAnalysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated.Originality/valueThe paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.

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Parking recommender system privacy preservation through anonymization and differential privacy

Cited by 14 publications

References 36 publications

A Secure and Privacy Preserved Parking Recommender System Using Elliptic Curve Cryptography and Local Differential Privacy

A Secure and Privacy Preserved Parking Recommender System Using Elliptic Curve Cryptography and Local Differential Privacy

Privacy-Preserving Matrix Factorization for Cross-Domain Recommendation

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

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