User's privacy in recommendation systems applying online social network data: a survey and taxonomy

Aghasian, Erfan; Garg, Saurabh; Montgomery, James

doi:10.1049/pbpc035f_ch12

Cited by 12 publications

(9 citation statements)

References 63 publications

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“…Privacy Assurance: Privacy assurance helps to preserve any private information, such as data, user, usage, locations, devices and network from unauthorized access [72][73][74]. In Fog Computing, all of the data used come from various sources like IoT devices, wireless networks as well as cloud networks.…”

Section: Privacy In Fog Computingmentioning

confidence: 99%

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

et al. 2021

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Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.

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Section: Privacy In Fog Computingmentioning

confidence: 99%

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

et al. 2021

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“…However, not all that glitters is gold, and the continuous gathering and processing of users' preferences along with their activities are demanding serious considerations about privacy concerns. Despite privacy issues in personalised and recommender systems being studied for a long time [6,7], it is in the last several years that more emphasis has been placed on this question, in all fields where personalised systems are used, as users were never really aware of the problem, especially about what personal data are being used and how securely it is stored [8][9][10]. A study conducted by the SAS company in July 2018 shows that almost three-fourths of the survey participants are more concerned about their data privacy now than they were in previous years, expressing worries, among other things, about personal information being shared without consent or its inappropriate use.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Privacy-Preserving Recommendations on Academic Graph Data

2021

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In the age of digital information, where the internet and social networks, as well as personalised systems, have become an integral part of everyone’s life, it is often challenging to be aware of the amount of data produced daily and, unfortunately, of the potential risks caused by the indiscriminate sharing of personal data. Recently, attention to privacy has grown thanks to the introduction of specific regulations such as the European GDPR. In some fields, including recommender systems, this has inevitably led to a decrease in the amount of usable data, and, occasionally, to significant degradation in performance mainly due to information no longer being attributable to specific individuals. In this article, we present a dynamic privacy-preserving approach for recommendations in an academic context. We aim to implement a personalised system capable of protecting personal data while at the same time allowing sensible and meaningful use of the available data. The proposed approach introduces several pseudonymisation procedures based on the design goals described by the European Union Agency for Cybersecurity in their guidelines, in order to dynamically transform entities (e.g., persons) and attributes (e.g., authored papers and research interests) in such a way that any user processing the data are not able to identify individuals. We present a case study using data from researchers of the Georg Eckert Institute for International Textbook Research (Brunswick, Germany). Building a knowledge graph and exploiting a Neo4j database for data management, we first generate several pseudoN-graphs, being graphs with different rates of pseudonymised persons. Then, we evaluate our approach by leveraging the graph embedding algorithm node2vec to produce recommendations through node relatedness. The recommendations provided by the graphs in different privacy-preserving scenarios are compared with those provided by the fully non-pseudonymised graph, considered as the baseline of our evaluation. The experimental results show that, despite the structural modifications to the knowledge graph structure due to the de-identification processes, applying the approach proposed in this article allows for preserving significant performance values in terms of precision.

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“…Figure 1 shows the prevalence of this phenomenon: approximately 200 different notions, inspired by DP, were defined in the last 15 years. 1 These DP definitions can be extensions or variants of DP. An extension encompasses the original DP notion as a special case, while a variant changes some aspect, typically to weaken or strengthen the original definition.…”

Section: Introductionmentioning

confidence: 99%

SoK: Differential privacies

Desfontaines

Pejó

2020

Proceedings on Privacy Enhancing Technologies

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Shortly after it was first introduced in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy definitions based on differential privacy, and partition them into seven categories, depending on which aspect of the original definition is modified.These categories act like dimensions: variants from the same category cannot be combined, but variants from different categories can be combined to form new definitions. We also establish a partial ordering of relative strength between these notions by summarizing existing results. Furthermore, we list which of these definitions satisfy some desirable properties, like composition, postprocessing, and convexity by either providing a novel proof or collecting existing ones.

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User's privacy in recommendation systems applying online social network data: a survey and taxonomy

Cited by 12 publications

References 63 publications

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

Dynamic Privacy-Preserving Recommendations on Academic Graph Data

SoK: Differential privacies

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