On Privacy Preserving Collaborative Filtering: Current Trends, Open Problems, and New Issues

Casino, Fran; Patsakis, Constantinos; Puig, Domènec; Solanas, Agustí

doi:10.1109/icebe.2013.37

Cited by 26 publications

(9 citation statements)

References 48 publications

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“…As previously stated, the curse of dimensionality has a significant impact on the k-anonymity model [176]. These days, due to the prevailing vast collection of data from mobile sensors and ubiquitous computing devices [246]- [249], high-dimensional data are not only found in healthcare anymore -in which traditionally PPDP is applied -but to other application domains as well. Hence, the development of proper anonymisation mechanisms that can be extended to many and diverse application areas becomes compulsory [250]- [253].…”

Section: Open Questions and Future Directionsmentioning

confidence: 99%

A Survey on Privacy Properties for Data Publishing of Relational Data

et al. 2020

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Recent advances in telecommunications and database systems have allowed the scientific community to efficiently mine vast amounts of information worldwide and to extract new knowledge by discovering hidden patterns and correlations. Nevertheless, all this shared information can be used to invade the privacy of individuals through the use of fusion and mining techniques. Simply removing direct identifiers such as name, SSN, or phone number is not anymore sufficient to prevent against these practices. In numerous cases, other fields, like gender, date of birth and/or zipcode, can be used to re-identify individuals and to expose their sensitive details, e.g. their medical conditions, financial statuses and transactions, or even their private connections. The scope of this work is to provide an in-depth overview of the current state of the art in Privacy-Preserving Data Publishing (PPDP) for relational data. To counter information leakage, a number of data anonymisation methods have been proposed during the past few years, including k-anonymity,-diversity, t-closeness, to name a few. In this study we analyse these methods providing concrete examples not only to explain how each of them works, but also to facilitate the reader to understand the different usage scenarios in which each of them can be applied. Furthermore, we detail several attacks along with their possible countermeasures, and we discuss open questions and future research directions.

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Section: Open Questions and Future Directionsmentioning

confidence: 99%

A Survey on Privacy Properties for Data Publishing of Relational Data

et al. 2020

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“…In the recommendation/prediction computation phase, we determine the neighborhood of and make a recommendation using well-known methods such as the ones proposed in [25]. The interested reader could refer to [26][27][28][29] to delve into CF's state of the art.…”

Section: Recommender Systems and Collaborativementioning

confidence: 99%

Performance Analysis of ZigBee Wireless Networks for AAL through Hybrid Ray Launching and Collaborative Filtering

et al. 2016

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This paper presents a novel hybrid simulation method based on the combination of an in-house developed 3D ray launching algorithm and a collaborative filtering (CF) technique, which will be used to analyze the performance of ZigBee-based wireless sensor networks (WSNs) to enable ambient assisted living (AAL). The combination of Low Definition results obtained by means of a deterministic ray launching method and the application of a CF technique leads to a drastic reduction of the time and computational cost required to obtain accurate simulation results. The paper also reports that this kind of AAL indoor complex scenario with multiple wireless devices needs a thorough and personalized radioplanning analysis as radiopropagation has a strong dependence on the network topology and the specific morphology of the scenario. The wireless channel analysis performed by our hybrid method provides valuable insight into network design phases of complex wireless systems, typical in AAL-oriented environments. Thus, it results in optimizing network deployment, reducing overall interference levels, and increasing the overall system performance in terms of cost reduction, transmission rates, and energy efficiency.

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“…The authors of Ref present a concise state‐of‐the‐art of the existing CF algorithms. They also classify PPCF algorithms in terms of different dimensions.…”

Section: Related Workmentioning

confidence: 99%

“…Ref presents a survey about PPCF covering studies until the year 2013 but without discussing the current and future trends. Ref discusses the current tendencies in the PPCF literature. Although the authors present some open problems, there is still a gap that needs to be filled because of changing trends.…”

Section: Introductionmentioning

confidence: 99%

From existing trends to future trends in privacy‐preserving collaborative filtering

Ozturk

Polat

2015

WIREs Data Min & Knowl

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The information overload problem, also known as infobesity, forces online vendors to utilize collaborative filtering algorithms. Although various recommendation methods are widely used by many electronic commerce sites, they still have substantial problems, including but not limited to privacy, accuracy, online performance, scalability, cold start, coverage, grey sheep, robustness, being subject to shilling attacks, diversity, data sparsity, and synonymy. Privacy-preserving collaborative filtering methods have been proposed to handle the privacy problem. Due to the increasing popularity of privacy protection and recommendation estimation over the Internet, prediction schemes with privacy are still receiving increasing attention. Because research trends might change over time, it is critical for researchers to observe future trends. In this study, we determine the existing trends in the privacypreserving collaborative filtering field by examining the related papers published mainly in the last few years. Comprehensive examinations of the most up-to-date related studies are described. By scrutinizing the contemporary inclinations, we present the most promising possible research trends in the near future. Our proposals can help interested researchers direct their research toward better outcomes and might open new ways to enrich privacy-preserving collaborative filtering studies.

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On Privacy Preserving Collaborative Filtering: Current Trends, Open Problems, and New Issues

Cited by 26 publications

References 48 publications

A Survey on Privacy Properties for Data Publishing of Relational Data

A Survey on Privacy Properties for Data Publishing of Relational Data

Performance Analysis of ZigBee Wireless Networks for AAL through Hybrid Ray Launching and Collaborative Filtering

From existing trends to future trends in privacy‐preserving collaborative filtering

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