PurposeThis study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the extent of adopting privacy-preserving RSs and postulates the future direction of research in RS security.Design/methodology/approachThe study gathered articles from well-known databases such as SCOPUS, Web of Science and Google scholar. A systematic literature review using PRISMA was carried out on the 41 papers that are shortlisted for study. Two research questions were framed to carry out the review.FindingsIt is evident from this study that privacy issues in the RS have been addressed with various techniques. However, many more challenges are expected while leveraging technology advancements for fine-tuning recommenders, and a research agenda has been devised by postulating future directions.Originality/valueThe study unveils a new comprehensive perspective regarding privacy preservation in recommenders. There is no promising study found that gathers techniques used for privacy protection. The study summarizes the research agenda, and it will be a good reference article for those who develop privacy-preserving RSs.

Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda

The recent advancement in next-generation Consumer Electronics (CE) has created the problems of information overload and information loss. The significance of Personalized Recommendation Systems (PRS) to efficiently and effectively extract useful user information is seen as an ideal solution to provide users with personalized content and services and therefore is used in different application domains including healthcare, e-commerce, social media, etc. Security and privacy are the two major challenges of the existing PRS for next-gen CE data. Federated learning (FL) has the potential to elevate the aforementioned challenges by sharing local recommender parameters while keeping all the training data on the device and therefore is seen as a promising technique to enhance security and privacy in PRS for the next-gen CE data. In this survey, we have first discussed the enhancement of the existing CE technologies, a holistic review of security and privacy challenges in current PRS, and the advantage of FL-based PRS for next-gen CE. Finally, we list a few open issues and challenges that can guide researchers and practitioners to further drive research in this promising area. 

Federated Learning-based Personalized Recommendation Systems: An Overview on Security and Privacy Challenges

In Multi-access Edge Computing (MEC) systems, service migration has been extensively applied to ensure service quality by migrating services to follow mobile users. The existing migration methods mainly focus on optimizing service response latency and migration costs by predicting user’s movements. However, some malicious adversaries can learn auxiliary knowledge, i.e., users’ mobility model and service migration trajectory, and launch location inference attacks to infer user locations. This leads to serious personal security threats, like malvertising, fraud and kidnapping. In this paper, we propose a location privacy-aware service migration method to against adversaries’ location inference attacks in multi-user MEC systems. First, we adopt an entropy-based location privacy metric to accurately measure user’s location privacy leakage risk. Then, we formulate the service migration progress as a joint optimization problem that minimizes service response latency and location privacy leakage risk. To cope with inter-user interference, we developed a multi-agent soft actor-critic (MASAC) algorithm to help users collaboratively make service migration decisions. Finally, simulations based on real-world user movement trajectories were conducted to demonstrate the superiority of the proposed method. Evaluation and analysis results showed that our proposed method can effectively protect user location privacy while maintaining a low service response latency. 

Location Privacy-aware Service Migration against Inference Attacks in Multi-user MEC Systems

This article explores the causes of the issues with personal data privacy and outlines the limitations of China's current legal framework. This article makes the argument that, in the information age, self-discipline and legal protection should be combined in order to safeguard personal information safety. It also makes specific recommendations for strengthening legal protection. This research also develops a data processing platform for data safety and privacy protection while studying the technology of data safety environment monitoring and privacy protection. This work develops optimization methodologies, such as dynamic privacy budget allocation, to increase the model's speed of convergence and the calibre of the generated data. It adjusts to various privacy and timeliness needs under the assumption that the objective of selective matching of private safety will be satisfied. According to the experimental findings, this algorithm's accuracy can reach 96.27%. This method enhances the model's speed of convergence and the calibre of the data created, and it addresses the flaw that the present data fusion publishing procedure cannot withstand the attack of background information. The study's findings can serve as a starting point for future work on data security and the protection of personal information.

/pdf/personal-information-security-environment-monitoring-and-law-2hn8vqet.pdf

Personal Information Security Environment Monitoring and Law Protection Using Big Data Analysis

With the development of Intelligent Transportation Systems (ITS), a vast amount of location data is being generated from various IoT devices equipped with location positioning sensors. Preserving the privacy of location data release is a critical concern, as the publication of aggregated data often reveals private information about the users. Differential Privacy (DP) has recently emerged as a robust framework to guarantee privacy in this context. However, conventional DP mechanisms commonly make no assumption about the distribution of the input data, which could lead to unexpected privacy leakage if the data are correlated. In this paper, we investigate the complex simultaneous impact of user correlation, spatial–temporal correlation and prior knowledge of an adversary on the privacy leakage of a DP mechanism, which has not been addressed in prior work. We derive several closed-form expressions that demonstrate and quantify the privacy leakage under correlated location data, followed by the design of efficient algorithms to compute such privacy leakage. Then, we propose a Δ-CDP (Correlated Differential Privacy) to provide a formal privacy guarantee against the additional privacy leakage incurred by these factors. Extensive comparisons, theoretical analysis, and experimental simulations are presented to validate the correctness and efficiency of the proposed work. 

May the privacy be with us: Correlated differential privacy in location data for ITS

The rapid development of the Global Positioning System (GPS) devices and location-based services (LBSs) facilitates the collection of huge amounts of personal information for the untrusted/unknown LBS providers This phenomenon raises serious privacy concerns However, most of the existing solutions aim at locating interference in the static scenes or in a single timestamp without considering the correlation between location transfer and time of moving users In this way, the solutions are vulnerable to various inference attacks Traditional privacy protection methods rely on trusted third-party service providers, but in reality, we are not sure whether the third party is trustable In this paper, we propose a systematic solution to preserve location information The protection provides a rigorous privacy guarantee without the assumption of the credibility of the third parties The user’s historical trajectory information is used as the basis of the hidden Markov model prediction, and the user’s possible prospective location is used as the model output result to protect the user’s trajectory privacy To formalize the privacy-protecting guarantee, we propose a new definition, L&A-location region, based on - anonymity and differential privacy Based on the proposed privacy definition, we design a novel mechanism to provide a privacy protection guarantee for the users’ identity trajectory We simulate the proposed mechanism based on a dataset collected in real practice The result of the simulation shows that the proposed algorithm can provide privacy protection to a high standard

/pdf/protecting-the-moving-user-s-locations-by-combining-5gf3yqtjo0.pdf

Protecting the Moving User’s Locations by Combining Differential Privacy and k -Anonymity under Temporal Correlations in Wireless Networks

The development of 5G technology has driven the rise of e-commerce, social networking, and the Internet of Things. Under the high-speed transmission, the data volume increases, and the user demand also changes. Personalized customization has become the mainstream trend of network development. However, as the speed of the Internet increases, a series of problems also arise. The increase in data volume results in a reduction of bandwidth, a growth of the central processor’s pressure, and a higher risk of data leakage. A search system and a recommendation platform are the tools to improve people’s search efficiency. However, providing personalized recommendations to different users according to their needs is still an urgent problem. Simultaneously, the big data volume means that attackers can also get more information. They can use background knowledge and various reasoning methods to deduce the user’s private information using nonprivate items. In this paper, the solutions to safe and reliable recommendation services are the main problem explored. Based on this idea, this paper proposed short-term dynamic recommendation model based on local differential privacy (SDRM-LDP). This model uses a small amount of user information to construct short-term user preference behaviors and provides recommendations for users based on the similarity between items. We consider that an attacker uses nonprivate items to derive privacy items. Therefore, we randomly replace the original data in the same category. At the same time, the local differential privacy (LDP) is added to the privacy item query to make the private data available and protect the privacy information. In this paper, two real-world datasets, ML-100K and ML-10M, are used for experiments. Experimental results show that the results of SDRM-LDP are superior to other models.

/pdf/sdrm-ldp-a-recommendation-model-based-on-local-differential-27fqlj27a9.pdf

SDRM-LDP: A Recommendation Model Based on Local Differential Privacy

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jishen Yang

Author Tools

Chat about Author

Papers

Protecting the Moving User’s Locations by Combining Differential Privacy and k -Anonymity under Temporal Correlations in Wireless Networks

SDRM-LDP: A Recommendation Model Based on Local Differential Privacy