Practical Privacy Preserving POI Recommendation

Chen, Chaochao; Zhou, Jun; Wu, Bingzhe; Fang, Wenjing; Wang, Li; Yuan, Qi; Zheng, Xiaolin

doi:10.1145/3394138

Cited by 51 publications

(19 citation statements)

References 55 publications

(84 reference statements)

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“…To reduce the computational cost of the privacy model, additive HE was used for PCA with a single data user [ 217 ], where the rank of PCA with an unknown distribution could be adaptively estimated to achieve (𝜖, 𝛿)-DP [ 218 ]. More recently, the concept of collaborative learning (or shared machine learning) [ 94 , 97 , 220 ] became very popular in data anonymization. That is, the data collected from multiple parties are collectively used to improve the performance of model training while protecting individual data owners from any information disclosure.…”

Section: Resultsmentioning

confidence: 99%

“…There were a total of 13 preprints among the 192 selected articles ( Figure 4 ) after phase 1. Before beginning phase 2, by applying the Gray Literature Checker mechanism, we observed that 4 of the 13 preprints had been successfully published in either peer-reviewed conferences [ 94 - 96 ] or journals [ 97 ]. Next, the Duplicates Checker was applied consecutively to remove their preprint versions.…”

Section: Methodsmentioning

confidence: 99%

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Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

Zuo¹,

Watson²,

Budgen³

et al. 2021

JMIR Med Inform

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Background Data science offers an unparalleled opportunity to identify new insights into many aspects of human life with recent advances in health care. Using data science in digital health raises significant challenges regarding data privacy, transparency, and trustworthiness. Recent regulations enforce the need for a clear legal basis for collecting, processing, and sharing data, for example, the European Union’s General Data Protection Regulation (2016) and the United Kingdom’s Data Protection Act (2018). For health care providers, legal use of the electronic health record (EHR) is permitted only in clinical care cases. Any other use of the data requires thoughtful considerations of the legal context and direct patient consent. Identifiable personal and sensitive information must be sufficiently anonymized. Raw data are commonly anonymized to be used for research purposes, with risk assessment for reidentification and utility. Although health care organizations have internal policies defined for information governance, there is a significant lack of practical tools and intuitive guidance about the use of data for research and modeling. Off-the-shelf data anonymization tools are developed frequently, but privacy-related functionalities are often incomparable with regard to use in different problem domains. In addition, tools to support measuring the risk of the anonymized data with regard to reidentification against the usefulness of the data exist, but there are question marks over their efficacy. Objective In this systematic literature mapping study, we aim to alleviate the aforementioned issues by reviewing the landscape of data anonymization for digital health care. Methods We used Google Scholar, Web of Science, Elsevier Scopus, and PubMed to retrieve academic studies published in English up to June 2020. Noteworthy gray literature was also used to initialize the search. We focused on review questions covering 5 bottom-up aspects: basic anonymization operations, privacy models, reidentification risk and usability metrics, off-the-shelf anonymization tools, and the lawful basis for EHR data anonymization. Results We identified 239 eligible studies, of which 60 were chosen for general background information; 16 were selected for 7 basic anonymization operations; 104 covered 72 conventional and machine learning–based privacy models; four and 19 papers included seven and 15 metrics, respectively, for measuring the reidentification risk and degree of usability; and 36 explored 20 data anonymization software tools. In addition, we also evaluated the practical feasibility of performing anonymization on EHR data with reference to their usability in medical decision-making. Furthermore, we summarized the lawful basis for delivering guidance on practical EHR data anonymization. Conclusions This systematic literature mapping study indicates that anonymization of EHR data is theoretically achievable; yet, it requires more research efforts in practical implementations to balance privacy preservation and usability to ensure more reliable health care applications.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

Zuo¹,

Watson²,

Budgen³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…An early work similar to ours is PriRec (Chen et al., 2020), where they propose a distributed privacy‐preserving POI recommendation framework. However, our work differs from PriRec in at least four ways.…”

Section: Related Workmentioning

confidence: 99%

A privacy‐preserving framework for location recommendation using decentralized collaborative machine learning

Rao

Gao

et al. 2021

Transactions in GIS

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The nowadays ubiquitous location‐aware mobile devices have contributed to the rapid growth of individual‐level location data. Such data are usually collected by location‐based service platforms as training data to improve their predictive models' performance, but the collection of such data may raise public concerns about privacy issues. In this study, we introduce a privacy‐preserving location recommendation framework based on a decentralized collaborative machine learning approach: federated learning. Compared with traditional centralized learning frameworks, we keep users' data on their own devices and train the model locally so that their data remain private. The local model parameters are aggregated and updated through secure multiple‐party computation to achieve collaborative learning among users while preserving privacy. Our framework also integrates information about transportation infrastructure, place safety, and flow‐based spatial interaction to further improve recommendation accuracy. We further design two attack cases to examine the privacy protection effectiveness and robustness of the framework. The results show that our framework achieves a better balance on the privacy–utility trade‐off compared with traditional centralized learning methods. The results and ensuing discussion offer new insights into privacy‐preserving geospatial artificial intelligence and promote geoprivacy in location‐based services.

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“…In short, these works focused on decreasing the RMSE during the learning process and attempted to predict users' preferences using explicit feedback that has a fixed scale rating. Some works adopted LDP for building recommendation systems [37], [44], [45]. Guo et al [37] proposed a privacy-preserving item-based CF technique under LDP.…”

Section: Related Workmentioning

confidence: 99%

“…However, the location preference here indicated whether a user wants to publish information about a specific location or not, which is a different area from recommending the next POI. Gao et al [45] proposed a POI recommendation model using Factorization Machines. They estimated the user preferences by combining a linear model with a high-order feature model and adopted decentralized SGD to train the model in a privacy-preserving manner.…”

Section: Related Workmentioning

confidence: 99%

Successive Point-of-Interest Recommendation With Local Differential Privacy

Kim

Chung

2021

IEEE Access

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A point-of-interest (POI) recommendation system performs an important role in locationbased services because it can help people to explore new locations and promote advertisers to launch advertisements at appropriate locations. The existing POI recommendation systems require raw checkin history of users, which might cause location privacy violations. Although there have been several matrix factorization (MF) based privacy-preserving recommendation systems, they can only focus on user-POI relationships without considering the human movements in check-in history. To tackle this problem, we design a successive POI recommendation framework with local differential privacy, named SPIREL. SPIREL uses two types of information derived from the check-in history as input for the factorization: a transition pattern between two POIs and the visit counts of POIs. We propose a novel objective function for learning the user-POI and POI-POI relationships simultaneously. We further integrate local differential privacy mechanisms in our proposed framework to prevent potential location privacy breaches. Experiments using four public datasets demonstrate that SPIREL achieves better POI recommendation quality while accomplishing stronger privacy protection.

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Practical Privacy Preserving POI Recommendation

Cited by 51 publications

References 55 publications

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

A privacy‐preserving framework for location recommendation using decentralized collaborative machine learning

Successive Point-of-Interest Recommendation With Local Differential Privacy

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