Utility-preserving differentially private skyline query

Lan, Qiujun; Ma, Jiaqi; Yan, Ziqi; Li, Gang

doi:10.1016/j.eswa.2021.115871

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…ese two measures have been broadly applied to evaluate the utility of noisy results relative to true query results. Finally, after the publisher performed the final skyline query, the PWLDP algorithm was compared with the LDP algorithm results by using the precision rate and F-measure, and the degree of privacy protection of this study's algorithm relative to the iDP-SC [25] algorithm is measured by the metric privacy leakage. To better avoid randomness, the algorithm query performance final metrics were based on the average of 1000 tests run in the same environment.…”

Section: Discussionmentioning

confidence: 99%

“…Ouyang et al [24] introduced a set-valued data collection approach (SetLDP) based on a category hierarchy under a local differential privacy model, whose central concept is to first to provide a random response to the presence of a category, and the results show that it can well protect the privacy information in the set-valued data. Lan et al [25] proposed personalized differential privacy (iDP-SC) based on a spectral clustering algorithm to reduce the local sensitivity by the introduction of the spectral clustering algorithm, and the noise reduction generated by spectral clustering compensates for the information distortion error introduced by itself. Xiong et al [26] proposed a new (ε, δ)-LDP concept for capturing users' privacy needs by accounting for the temporal relevance of spatiotemporal data at the same time as guaranteeing sensible utility, demonstrating its superiority in achieving a better trade-off between privacy and utility for real-time spatiotemporal data integration and rigorous privacy protection.…”

Section: Related Workmentioning

confidence: 99%

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Support Personalized Weighted Local Differential Privacy Skyline Query

Zhang

Chen

Jia

et al. 2022

Security and Communication Networks

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The potential privacy risks in certain situations are of concern because of the frequent sharing of data during skyline queries, leading to leakage of users’ private information. The most common privacy-preserving technique is to anonymize data by removing or changing certain information, for which an attack with specific background knowledge would render the privacy protection ineffective. To overcome these difficulties, this study proposes a personalized weighted local differential privacy method (PWLDP) to protect data privacy during skyline querying. Compared with existing studies of skyline queries under privacy protection, the degree of privacy protection can be quantitatively analyzed, and the processing of data privacy lies with the user, who quantitatively perturbs the processing according to the sensitivity of the weights of different attributes to avoid substantial information loss. The performance of the proposed PWLDP is verified by comparing PWLDP and LDP on different datasets, the average privacy leakage reduction of 62.22% and 51.67% is obtained for experiments conducted on different datasets relative to the iDP-SC algorithm, and the experimental results demonstrate the efficiency and advantages of the proposed method.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Support Personalized Weighted Local Differential Privacy Skyline Query

Zhang

Chen

Jia

et al. 2022

Security and Communication Networks

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“…Individual differential privacy via spectral clustering iDP-SC has been proposed to ensure higher utility while preserving privacy (Lan et al, 2022). Some multicriteria decision-making methods use skyline query, which is prone to privacy leakage, and IDP-SC could be the solution to enhance privacy (Lan et al, 2022). Zhou et al (2019) conducted a comparative study of privacy mechanisms based on the operation mode, formal security model, generic constructions of lightweight privacypreserving RS and verification.…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…However, data utility was found deteriorating. Individual differential privacy via spectral clustering iDP-SC has been proposed to ensure higher utility while preserving privacy (Lan et al , 2022). Some multicriteria decision-making methods use skyline query, which is prone to privacy leakage, and IDP-SC could be the solution to enhance privacy (Lan et al , 2022).…”

Section: Techniquesmentioning

confidence: 99%

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

Pramod

2022

DTA

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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.

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“…The proposed method utilizes the clustering concept jointly with the DP in order to preserve the privacy of smart meter data. Lan et al [121] developed a novel anonymization method for skyline queries. The proposed method was experimentally tested on synthetic and real data.…”

Section: Individual Privacy Preservation and Sota Approaches In Track Cmentioning

confidence: 99%

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

2022

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Introduction: Recently, the tendency of artificial intelligence (AI) and big data use/applications is has been rapidly expanding across the globe, improving people’s lifestyles with data-driven services (i.e., recommendations, smart healthcare, etc.). The synergy between AI and big data has become imperative considering the drastic growth in personal data stemming from diverse sources (cloud computing, IoT, social networks, etc.). However, when data meet AI at some central place, it invites unimaginable privacy issues, and one of those issues is group privacy. Despite being the most significant problem, group privacy has not yet received the attention of the research community it is due. Problem Statement: We study how to preserve the privacy of particular groups (a community of people with some common attributes/properties) rather than an individual in personal data handling (i.e., sharing, aggregating, and/or performing analytics, etc.), especially when we talk about groups purposely made by two or more people (with clear group identifying markers), for whom we need to protect their privacy as a group. Aims/Objectives: With this technical letter, our aim is to introduce a new dimension of privacy (e.g., group privacy) from technical perspectives to the research community. The main objective is to advocate the possibility of group privacy breaches when big data meet AI in real-world scenarios. Methodology: We set a hypothesis that group privacy (extracting group-level information) is a genuine problem, and can likely occur when AI-based techniques meet high dimensional and large-scale datasets. To prove our hypothesis, we conducted a substantial number of experiments on two real-world benchmark datasets using AI techniques. Based on the experimental analysis, we found that the likelihood of privacy breaches occurring at the group level by using AI techniques is very high when data are sufficiently large. Apart from that, we tested the parameter effect of AI techniques and found that some parameters’ combinations can help to extract more and fine-grained data about groups. Findings: Based on experimental analysis, we found that vulnerability of group privacy can likely increase with the data size and capacity of the AI method. We found that some attributes of people can act as catalysts in compromising group privacy. We suggest that group privacy should also be given due attention as individual privacy is, and robust tools are imperative to restrict implications (i.e., biased decision making, denial of accommodation, hate speech, etc.) of group privacy. Significance of results: The obtained results are the first step towards responsible data science, and can pave the way to understanding the phenomenon of group privacy. Furthermore, the results contribute towards the protection of motives/goals/practices of minor communities in any society. Concluding statement: Due to the significant rise in digitation, privacy issues are mutating themselves. Hence, it is vital to quickly pinpoint emerging privacy threats and suggest practical remedies for them in order to mitigate their consequences on human beings.

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Utility-preserving differentially private skyline query

Cited by 5 publications

References 26 publications

Support Personalized Weighted Local Differential Privacy Skyline Query

Support Personalized Weighted Local Differential Privacy Skyline Query

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

Group Privacy: An Underrated but Worth Studying Research Problem in the Era of Artificial Intelligence and Big Data

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