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Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees
2017
IEEE Transactions on Information Forensics and Security
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Differential privacy is a popular privacy model within the research community because of the strong privacy guarantee it offers, namely that the presence or absence of any individual in a data set does ...
In an attempt to address this shortcoming, several relaxations of differential privacy have been proposed that trade off privacy guarantees for improved data utility. ...
Acknowledgments and disclaimer We thank Frank McSherry and Daniel Kifer for their comments and suggestions on individual differential privacy. ...
doi:10.1109/tifs.2017.2663337
fatcat:ssoraexrdre53o45cglfgajcqe
Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees
[article]
2022
arXiv
pre-print
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To account for this need, we propose two novel methods based on the Private Aggregation of Teacher Ensembles (PATE) framework to support the training of ML models with individualized privacy guarantees ...
We formally describe the methods, provide a theoretical analysis of their privacy bounds, and experimentally evaluate their effect on the final model's utility using the MNIST, SVHN, and Adult income datasets ...
ACKNOWLEDGMENTS This work is supported by the German Federal Ministry of Education and Research (grant 16SV8463: WerteRadar). ...
arXiv:2202.10517v4
fatcat:twbanh2qzzgadifbrbezewtboq
Privacy-Preserving Monotonicity of Differential Privacy Mechanisms
2018
Applied Sciences
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Differential privacy mechanisms can offer a trade-off between privacy and utility by using privacy metrics and utility metrics. ...
To this end, we proposed the definition of privacy-preserving monotonicity of differential privacy, which measured the trade-off between privacy and utility. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app8112081
fatcat:ulrsk4znezbj5ayb4fyg5s6j54
Data Anonymization Approach for Data Privacy
2015
International Journal of Science and Research (IJSR)
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At the time of released data differential privacy preserving mechanism support for individual data hiding , by adding the noise and disclose for the secondary purpose. ...
Here this are two factor can be help to maximizing the data utility as well as minimizing the risk by using differential privacy preserving method. ...
Disclosing the minimum amount of information or no information at all is try to protect the privacy of individual to whom data pertains [1] .The differential privacy preserving algorithm provide a personalized ...
doi:10.21275/v4i12.12121502
fatcat:bk4geatopngtrm4uwrxwxkdn2i
Privacy-Preserving Bin-Packing with Differential Privacy
2022
IEEE Open Journal of Signal Processing
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The noise decreases the utility of the dataset while preserving users' privacy by adding uncertainty to the dataset. ...
With a larger , less noise is added to the accurate data, so the algorithm has a weak privacy guarantee and good utility for the optimization work. ...
His research focuses on privacy-preserving techniques using differential privacy and cryptographic tools. Zekeriya Erkin (M'07-SM '18) ...
doi:10.1109/ojsp.2022.3153231
fatcat:wrs2akoxw5abhmhnqnts6n4toy
Privacy against statistical inference
2012
2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
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We prove that under both metrics the resulting design problem of finding the optimal mapping from the user's data to a privacy-preserving output can be cast as a modified rate-distortion problem which, ...
Finally, we compare our framework with differential privacy. ...
The design problem of finding privacy-preserving mappings for minimizing the information leakage from a user's data with utility constraints was formulated as a convex program. ...
doi:10.1109/allerton.2012.6483382
dblp:conf/allerton/CalmonF12
fatcat:tpgo3dwlrvh5lkmqpihghpjuhy
Privacy Against Statistical Inference
[article]
2012
arXiv
pre-print
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We prove that under both metrics the resulting design problem of finding the optimal mapping from the user's data to a privacy-preserving output can be cast as a modified rate-distortion problem which, ...
Finally, we compare our framework with differential privacy. ...
The design problem of finding privacy-preserving mappings for minimizing the information leakage from a user's data with utility constraints was formulated as a convex program. ...
arXiv:1210.2123v1
fatcat:lifka4lp5zcjbhclriqtc6vw3a
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
[article]
2020
arXiv
pre-print
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In this paper, we show that differential privacy can do more than just privacy preservation. ...
Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. ...
Differential privacy, with its theoretical guarantee of utility of querying results, may be a promising technique for privacy-preserving reasoning. ...
arXiv:2008.01916v1
fatcat:ujmxv7eq6jcppndfu5shbzkdom
XYZ Privacy
[article]
2018
arXiv
pre-print
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XYZ Privacy is to our knowledge the first such mechanism that enables data creators to submit multiple contradictory responses to a query, whilst preserving utility measured as the absolute error from ...
For instance, individual location data can be obfuscated while preserving utility, thereby enabling the scheme to transparently integrate with existing systems (e.g. Waze). ...
the differential privacy guarantee. ...
arXiv:1710.03322v5
fatcat:sxodr5x6bzhktezpus6plglnly
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
2020
IEEE Transactions on Knowledge and Data Engineering
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In this paper, we show that differential privacy can do more than just privacy preservation. ...
Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. ...
Differential privacy, with its theoretical guarantee of utility of querying results, may be a promising technique for privacy-preserving reasoning. ...
doi:10.1109/tkde.2020.3014246
fatcat:33rl6jxy5rgexpnuel5rvlkg5a
Security Versus Privacy
2018
2018 IEEE Conference on Decision and Control (CDC)
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Therefore, by increasing the level of privacy, the security guarantees can only be weakened and vice versa. Similar results are developed under the differential privacy framework. ...
The server provides a response to the queries systematically corrupted using an additive noise to preserve the privacy of those whose data is stored on the server. ...
for preserving the privacy of individuals [6] - [8] . ...
doi:10.1109/cdc.2018.8619460
dblp:conf/cdc/FarokhiE18
fatcat:6abv6iuzefdsbjisaharuun3iq
Privacy Games: Optimal User-Centric Data Obfuscation
2015
Proceedings on Privacy Enhancing Technologies
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We optimize utility subject to a joint guarantee of differential privacy (indistinguishability) and distortion privacy (inference error). ...
The entanglement of the utility loss and the privacy guarantee, in addition to the lack of a comprehensive notion of privacy, have led to the design of obfuscation mechanisms that are either suboptimal ...
Thanks to our unified formulation of privacy optimization problems as linear programs, the problem of jointly optimizing and guaranteeing privacy with both metrics can also be formulated as a linear program ...
doi:10.1515/popets-2015-0024
dblp:journals/popets/Shokri15
fatcat:dzulb6iqkva3faphtr73ilwr5q
Heterogeneous Differential Privacy
[article]
2015
arXiv
pre-print
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The massive collection of personal data by personalization systems has rendered the preservation of privacy of individuals more and more difficult. ...
The results of our experiments demonstrate that heterogeneous differential privacy can account for different privacy attitudes while sustaining a good level of utility as measured by the recall for the ...
The results obtained show that the proposed approach can still sustain a high utility level (as measured in terms of recall) while guaranteeing heterogeneous differential privacy. ...
arXiv:1504.06998v1
fatcat:tvwetuijsfhlldm26hxdjhgehu
Differential Privacy in Practice
2013
Journal of Computing Science and Engineering
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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
Privacy-Preserving Collaborative Web Services QoS Prediction via Differential Privacy
[chapter]
2017
Lecture Notes in Computer Science
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We introduce differential privacy, a rigorous and provable privacy preserving technique, into the preprocess of QoS data prediction. ...
In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. ...
To sum up, the main contribution of this work is to formulate a differential privacy based privacy preserving collaborative Web Services QoS prediction. ...
doi:10.1007/978-3-319-63579-8_16
fatcat:h2o2qkbwtvdthfikaxtc5csyr4
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