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Linear and Range Counting under Metric-based Local Differential Privacy [article]

Zhuolun Xiang, Bolin Ding, Xi He, Jingren Zhou
2020 arXiv   pre-print
We show that, under such privacy relaxations, for analytical workloads such as linear counting, multi-dimensional range counting queries, and quantile queries, we can achieve significant gains in utility  ...  Local differential privacy (LDP) enables private data sharing and analytics without the need for a trusted data collector.  ...  [26] establish an equivalence relationship between Blowfish privacy for answering linear counting queries and standard -differential privacy for answering transformed linear counting queries, under  ... 
arXiv:1909.11778v3 fatcat:cjbhtzoozrf6fbjjijreqg53v4

Private Graph Data Release: A Survey [article]

Yang Li, Michael Purcell, Thierry Rakotoarivelo, David Smith, Thilina Ranbaduge, Kee Siong Ng
2021 arXiv   pre-print
Many of these mechanisms fall under natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that can deal  ...  with the limitations of Differential Privacy.  ...  [131] presented the earliest mechanisms for releasing private triangle, three-hop path, and k-clique counts in a localized setting called decentralized differential privacy under the edge differential  ... 
arXiv:2107.04245v1 fatcat:kixgz52kejarhjt6sbrkfy4cga

Differential Privacy in Practice

Hiep H. Nguyen, Jong Kim, Yoonho Kim
2013 Journal of Computing Science and Engineering  
We briefly review the problem of statistical disclosure control under differential privacy model, which entails a formal and ad omnia privacy guarantee separating the utility of the database and the risk  ...  Promises of differential privacy help to relieve concerns of privacy loss, which hinder the release of community-valuable data.  ...  ACKNOWLEDGMENTS This research was supported by World Class University program funded by the Ministry of Education, Science and Technology through the National Research Foundation of Korea (R31-10100).  ... 
doi:10.5626/jcse.2013.7.3.177 fatcat:xrcbyxpzfvh2tfdrr5572rdnku

R^2DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions [article]

Meisam Mohammady, Shangyu Xie, Yuan Hong, Mengyuan Zhang, Lingyu Wang, Makan Pourzandi, Mourad Debbabi
2020 arXiv   pre-print
Differential privacy (DP) has emerged as a de facto standard privacy notion for a wide range of applications.  ...  Specifically, we define a universal framework, namely, randomizing the randomization mechanism of differential privacy (R^2DP), and we formally analyze its privacy and utility.  ...  This work is partially supported by the Natural Sciences and Engineering Research Council of Canada and Ericsson Canada under the Industrial Research Chair (IRC) in SDN/NFV Security.  ... 
arXiv:2009.09451v2 fatcat:zzxr3vba5bdkla3vlu63u5nduy

Local Differential Privacy and Its Applications: A Comprehensive Survey [article]

Mengmeng Yang, Lingjuan Lyu, Jun Zhao, Tianqing Zhu, Kwok-Yan Lam
2020 arXiv   pre-print
This survey provides a comprehensive and structured overview of the local differential privacy technology.  ...  We discuss the practical deployment of local differential privacy and explore its application in various domains.  ...  [57, 58] first discuss the range query under local differential privacy setting.  ... 
arXiv:2008.03686v1 fatcat:l7z3gip2ivdmvin7lraxd4vciy

Using Metrics Suites to Improve the Measurement of Privacy in Graphs

Yuchen Zhao, Isabel Wagner
2020 IEEE Transactions on Dependable and Secure Computing  
privacy for stronger adversaries; for within-scenario comparisons, evenness indicates whether metric values are spread evenly; and for between-scenario comparisons, shared value range indicates whether  ...  metrics use a consistent value range across scenarios.  ...  ACKNOWLEDGMENTS This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/P006752/1 and used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk  ... 
doi:10.1109/tdsc.2020.2980271 fatcat:nydqxcet7jfh3fjazqc7ets7a4

Differential privacy with δ-neighbourhood for spatial and dynamic datasets

Chengfang Fang, Ee-Chien Chang
2014 Proceedings of the 9th ACM symposium on Information, computer and communications security - ASIA CCS '14  
For dynamic datasets, while there are known negative results on the standard differential privacy, it is possible to continuously and indefinitely publish under δ-neighbourhood by reusing the privacy budgets  ...  In addition, we give mechanisms that achieve "sustainable privacy" on dynamic datasets under both online and offline setting.  ...  Differential Privacy under δ-Neighbourhood We say that a mechanism A is -differential privacy under δ-neighbourhood if for all R ⊆ range(A) and any pair of δ-neighbours (D1, D2): P r(A(D1) ∈ R) ≤ exp(  ... 
doi:10.1145/2590296.2590320 dblp:conf/ccs/FangC14 fatcat:6ogho2wslfgddizbqd2jmuudsa

A Comprehensive Survey on Local Differential Privacy

Xingxing Xiong, Shubo Liu, Dan Li, Zhaohui Cai, Xiaoguang Niu
2020 Security and Communication Networks  
Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while  ...  application fields under LDP.  ...  [121] studied the problem of privately answering range queries and achieving frequency estimation with LDP and developed a linear-equations-based mechanism, which satisfies local d-privacy [122] (  ... 
doi:10.1155/2020/8829523 fatcat:xjk3vgyambb5xioc2q5hyr2hua

Not All Attributes are Created Equal: d_X-Private Mechanisms for Linear Queries [article]

Parameswaran Kamalaruban and Victor Perrier and Hassan Jameel Asghar and Mohamed Ali Kaafar
2019 arXiv   pre-print
Our proposed meta procedure has broad applications as linear queries form the basis of a range of data analysis and machine learning algorithms, and the ability to define a more flexible privacy budget  ...  Differential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets.  ...  ., [2] where the authors focus on location-based systems in the local model, and [6] which only considers universally optimal mechanisms under some specific d X metrics.  ... 
arXiv:1806.02389v2 fatcat:ugp56avfgrcppncueozehiqgqe

Not All Attributes are Created Equal: dX -Private Mechanisms for Linear Queries

Parameswaran Kamalaruban, Victor Perrier, Hassan Jameel Asghar, Mohamed Ali Kaafar
2020 Proceedings on Privacy Enhancing Technologies  
Our proposed meta procedure has broad applications as linear queries form the basis of a range of data analysis and machine learning algorithms, and the ability to define a more flexible privacy budget  ...  Differential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets.  ...  ., [2] where the authors focus on location-based systems in the local model, and [6] which only considers universally optimal mechanisms under some specific d X metrics.  ... 
doi:10.2478/popets-2020-0007 dblp:journals/popets/KamalarubanPAK20 fatcat:vmdcveafofb4fcgf5zpybs7ulq

Locality Sensitive Hashing with Extended Differential Privacy [article]

Natasha Fernandes, Yusuke Kawamoto, Takao Murakami
2021 arXiv   pre-print
Extended differential privacy, a generalization of standard differential privacy (DP) using a general metric, has been widely studied to provide rigorous privacy guarantees while keeping high utility.  ...  Our mechanisms are based on locality sensitive hashing (LSH), which can be applied to the angular distance and work well for personal data in a high-dimensional space.  ...  Xiang, Z., Ding, B., He, X., Zhou, J.: Linear and range counting under metric-based local differential privacy. In: ISIT. pp. 908-913 (2020) 54.  ... 
arXiv:2010.09393v5 fatcat:6irdtis2dzdshpplwfzoi44izq

Research Challenges in Designing Differentially Private Text Generation Mechanisms

Oluwaseyi Feyisetan, Abhinav Aggarwal, Zekun Xu, Nathanael Teissier
2021 Proceedings of the ... International Florida Artificial Intelligence Research Society Conference  
the noise based on the local region around a word.  ...  Such mechanisms add privacy preserving noise to vectorial representations of text in high dimension and return a text based projection of the noisy vectors.  ...  With text however, the sensitivity is much larger and is driven by the richness of the vocabulary, and how it is represented in the metric space under consideration.  ... 
doi:10.32473/flairs.v34i1.128461 fatcat:mcse4lzmjjfcveqgptzlgrttiy

A Differentially Private Framework for Spatial Crowdsourcing with Historical Data Learning [article]

Shun Zhang, Benfei Duan, Zhili Chen, Hong Zhong, Qizhi Yu
2020 arXiv   pre-print
We simulate locations by sampling the probability distribution learned from historical data, use them for grid partition, and then publish real-time data under this partitioning with differential privacy  ...  However, existing SC models with differential privacy usually perturb real-time location data for both partition and data publication.  ...  BACKGROUND In this section, we introduce some notations and initial definitions, and review spatial crowdsourcing, differential privacy, and linear regression method.  ... 
arXiv:2008.03475v3 fatcat:sytbalwginaczfig3kfwsaydky

Privacy Games: Optimal User-Centric Data Obfuscation

Reza Shokri
2015 Proceedings on Privacy Enhancing Technologies  
We optimize utility subject to a joint guarantee of differential privacy (indistinguishability) and distortion privacy (inference error).  ...  ., location-based) service. Data obfuscation is a prevalent user-centric approach to protecting users' privacy in such systems: the untrusted entity only receives a noisy version of user's data.  ...  Acknowledgements We would like to thank the PC reviewers for their constructive feedback, and Kostas Chatzikokolakis for very useful discussions on this work.  ... 
doi:10.1515/popets-2015-0024 dblp:journals/popets/Shokri15 fatcat:dzulb6iqkva3faphtr73ilwr5q

Adaptive Gaussian Mechanism Based on Expected Data Utility under Conditional Filtering Noise

2018 KSII Transactions on Internet and Information Systems  
Differential privacy has broadly applied to statistical analysis, and its mainly objective is to ensure the tradeoff between the utility of noise data and the privacy preserving of individual's sensitive  ...  However, an individual could not achieve expected data utility under differential privacy mechanisms, since the adding noise is random.  ...  In the future work, we will define better expected data utility metrics, and propose a general adaptive differential privacy framework based on these expected data utility metrics.  ... 
doi:10.3837/tiis.2018.07.027 fatcat:wt4rwmhg5ra45ao4awsz24k2ae
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