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Maximizing Privacy under Data Distortion Constraints in Noise Perturbation Methods [chapter]

Yaron Rachlin, Katharina Probst, Rayid Ghani
2009 Lecture Notes in Computer Science  
This paper introduces the 'guessing anonymity,' a definition of privacy for noise perturbation methods.  ...  This work addresses an important shortcoming of noise perturbation methods, by providing them with an intuitive definition of privacy analogous to the definition used in k-anonymity, and an analytical  ...  Using this definition, we formulate optimization problems for finding parameters of noise perturbation models that maximize privacy under data distortion constraints.  ... 
doi:10.1007/978-3-642-01718-6_7 fatcat:semgenp4ibb5nlw52kkfxqyi2u

GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel

Hai Liu, Changgen Peng, Youliang Tian, Shigong Long, Feng Tian, Zhenqiang Wu
2022 Entropy  
The existing work has conducted in-depth research and analysis on global differential privacy (GDP) and local differential privacy (LDP) based on information theory.  ...  Third, we summarized and analyzed definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP under their channel models.  ...  According to the rate-distortion function, References [34, 35, 37] maximized mutual information under expected Hamming distortion D constraint and obtained privacy budget ε = log 1−D D for binary channel  ... 
doi:10.3390/e24030430 pmid:35327940 pmcid:PMC8953244 fatcat:fyy54vlqdjhavjzajtvmn4qrwm

A Defense Framework for Privacy Risks in Remote Machine Learning Service

Yang Bai, Yu Li, Mingchuang Xie, Mingyu Fan, Jiang Ming
2021 Security and Communication Networks  
Differential privacy methods can defend against privacy threats from both the curious sever and the malicious MLaaS user by directly adding noise to the training data.  ...  In addition, our method can achieve better privacy and utility balance compared to the existing method.  ...  In this module, the constraint conditions will be sent to the adversarial perturbation generator module for noise production parameter adjustment direction.  ... 
doi:10.1155/2021/9924684 fatcat:fqanrrvdcrf3feqomhdwkezxwy

Privacy-Enhanced Architecture for Occupancy-based HVAC Control [article]

Ruoxi Jia, Roy Dong, S. Shankar Sastry, Costas J. Spanos
2016 arXiv   pre-print
To ensure privacy, we design an architecture that distorts the occupancy data in order to hide individual occupant location information while maintaining HVAC performance.  ...  We evaluate our framework using real-world occupancy data: first, we verify that our privacy metric accurately assesses the adversary's ability to infer private variables from the distorted sensor measurements  ...  perturbed data by other distortion methods.  ... 
arXiv:1607.03140v1 fatcat:yivzn3vklne4xlxacpbaxj2sg4

Generative Adversarial Privacy [article]

Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal
2019 arXiv   pre-print
Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holder to learn the privatization mechanism directly from the data.  ...  We present a data-driven framework called generative adversarial privacy (GAP).  ...  The distortion constraint is enforced by the penalty method as detailed in supplement B (see (17) ).  ... 
arXiv:1807.05306v3 fatcat:pmddbotq4jccpkij4mezdt7cyq

An information-theoretic approach to privacy

Lalitha Sankar, S. Raj Rajagopalan, H. Vincent Poor
2010 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)  
Ensuring the usefulness of electronic data sources while providing necessary privacy guarantees is an important unsolved problem.  ...  State of the art approaches have predominantly focused on privacy.  ...  Privacy, on the other hand, is maximized when the perturbed response is completely independent of the data.  ... 
doi:10.1109/allerton.2010.5707053 fatcat:gghq4swwmzfyxg2hmb46qojg7y

An Information-theoretic Approach to Privacy [article]

Lalitha Sankar, S. Raj Rajagopalan, H. Vincent Poor
2010 arXiv   pre-print
Ensuring the usefulness of electronic data sources while providing necessary privacy guarantees is an important unsolved problem.  ...  State of the art approaches have predominantly focused on privacy.  ...  Privacy, on the other hand, is maximized when the perturbed response is completely independent of the data.  ... 
arXiv:1010.0226v1 fatcat:iakcbb6bhzdxtiybyyikincs3i

Utility and Privacy of Data Sources: Can Shannon Help Conceal and Reveal Information? [article]

Lalitha Sankar, S. Raj Rajagopalan, H. Vincent Poor
2010 arXiv   pre-print
, quantifying utility-privacy tradeoffs irrespective of the type of data sources or the methods of providing privacy, developing a side-information model for dealing with questions of external knowledge  ...  Rate distortion theory is shown to be a natural choice to develop such a framework which includes the following: modeling of data sources, developing application independent utility and privacy metrics  ...  Privacy, on the other hand, is maximized when the perturbed response is completely independent of the data.  ... 
arXiv:1002.1347v1 fatcat:63yqm4fsyngo3jdskgsthwatti

Utility and privacy of data sources: Can Shannon help conceal and reveal information?

Lalitha Sankar, S. Raj Rajagopalan, H. Vincent Poor
2010 2010 Information Theory and Applications Workshop (ITA)  
, quantifying utility-privacy tradeoffs irrespective of the type of data sources or the methods of providing privacy, developing a side-information model for dealing with questions of external knowledge  ...  Rate distortion theory is shown to be a natural choice to develop such a framework which includes the following: modeling of data sources, developing application independent utility and privacy metrics  ...  Privacy, on the other hand, is maximized when the perturbed response is completely independent of the data.  ... 
doi:10.1109/ita.2010.5454092 dblp:conf/ita/SankarRP10 fatcat:pqyddp57zra6nkyfd56fjc3xue

A theory of utility and privacy of data sources

Lalitha Sankar, S. Raj Rajagopalan, H. Vincent Poor
2010 2010 IEEE International Symposium on Information Theory  
tradeoffs irrespective of the type of data sources or the methods of providing privacy, and developing a side-information model for dealing with questions of external knowledge.  ...  Rate distortion theory is shown to be a natural choice to develop such a framework which includes modeling of data sources, developing application independent utility and privacy metrics, quantifying utilityprivacy  ...  Privacy, on the other hand, is maximized when the perturbed response is completely independent of the data.  ... 
doi:10.1109/isit.2010.5513684 dblp:conf/isit/SankarRP10 fatcat:ifjb6dtafnh2dodsef4h7fkarm

Smart meter privacy: A utility-privacy framework

S. Raj Rajagopalan, Lalitha Sankar, Soheil Mohajer, H. Vincent Poor
2011 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm)  
End-user privacy in smart meter measurements is a well-known challenge in the smart grid.  ...  in power, and this approach appears to encompass most of the proposed privacy approaches.  ...  Privacy, on the other hand, is maximized when the perturbed data is completely independent of the original.  ... 
doi:10.1109/smartgridcomm.2011.6102315 dblp:conf/smartgridcomm/RajagopalanSMP11 fatcat:ehkzhf6s3fewjojobkxka4peq4

Hiding in the Crowd: Privacy Preservation on Evolving Streams through Correlation Tracking

Feifei Li, Jimeng Sun, Spiros Papadimitriou, George A. Mihaila, Ioana Stanoi
2007 2007 IEEE 23rd International Conference on Data Engineering  
We show that it is possible to efficiently and effectively track the correlation and autocorrelation structure of multivariate streams and leverage it to add noise which maximally preserves privacy, in  ...  We address the problem of preserving privacy in streams, which has received surprisingly limited attention.  ...  in designing additive random perturbation techniques, 3) design efficient online algorithms under the additive random perturbation framework, which maximally preserve the privacy of data streams given  ... 
doi:10.1109/icde.2007.367914 dblp:conf/icde/LiSPMS07 fatcat:7v44uv2m5ja25ouskkfwcdvw2y

Privacy Games: Optimal User-Centric Data Obfuscation

Reza Shokri
2015 Proceedings on Privacy Enhancing Technologies  
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.  ...  Perturbing data before sharing it, however, comes at the price of the users' utility (service quality) experience which is an inseparable design factor of obfuscation mechanisms.  ...  Regarding user-centric obfuscation mechanisms, [52] solves the problem of maximizing distortion privacy under a constraint on utility loss.  ... 
doi:10.1515/popets-2015-0024 dblp:journals/popets/Shokri15 fatcat:dzulb6iqkva3faphtr73ilwr5q

Privacy Preserving Clustering by Data Transformation

Stanley R. M. Oliveira, Osmar R. Zaïane
2010 Journal of Information and Data Management  
Our proposed methods distort only confidential numerical attributes to meet privacy requirements, while preserving general features for clustering analysis.  ...  We focus primarily on privacy preserving data clustering, notably on partitionbased and hierarchical methods.  ...  The Translation Data Perturbation Method In the Translation Data Perturbation Method, denoted by TDP, the observations of confidential attributes in each v i ∈ V are perturbed using an additive noise perturbation  ... 
dblp:journals/jidm/OliveiraZ10 fatcat:a4bauateibbkfjjhqx5yx5pzbe

Context-Aware Generative Adversarial Privacy

Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal
2017 Entropy  
Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on  ...  For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal  ...  The distortion constraint is enforced by the penalty method provided in (13) .  ... 
doi:10.3390/e19120656 fatcat:zgw3b4hsmjhczg62obrq6wocje
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