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Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy
[article]
2021
arXiv
pre-print
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Our mechanism can be interpreted as a locally-optimal greedy algorithm for a given processing order of sequence positions, where utility is measured by the number of positions released without erasure. ...
We introduce an erasure-based privacy mechanism with perfect information-theoretic privacy, whereby the released sequence is statistically independent of the sensitive genotypes. ...
We refer to this requirement as the privacy condition. Note that our notion of privacy is stronger than alternatives such as local differential privacy [33] , which allows a small amount of leakage. ...
arXiv:2007.05139v4
fatcat:bsz6ta6b3fhhregl3exipbe5vy
Hide me Behind the Noise: Local Differential Privacy for Indoor Location Privacy
[article]
2022
arXiv
pre-print
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2022
2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)
unpublished
doi:10.1109/eurospw55150.2022.00061 fatcat:5tpqueh4zngb7ig3weddra7pke
doi:10.1109/eurospw55150.2022.00061 fatcat:5tpqueh4zngb7ig3weddra7pke
This paper proposes a novel privacy-aware framework for aggregating the indoor location data employing the Local Differential Privacy (LDP) technique, in which the user location data is changed locally ...
The impact of dataset properties, the privacy mechanisms, and the privacy level on our framework are also investigated. ...
Definition 2.1 (ε-Local Differential Privacy (ε-LDP) [9]). ...
arXiv:2207.00633v1
fatcat:7k4n5y5x2nd4zc7d64y3dpnj6e
Hiding in the Crowd
2018
Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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We also confirm that the current evolution of web technologies is benefiting users' privacy significantly as the removal of plugins brings down substantively the rate of unique desktop machines. ...
A set represents a group of fingerprints with identical values for all the collected attributes. ...
[22] by putting the browser fingerprinting domain under a different light. ...
doi:10.1145/3178876.3186097
dblp:conf/www/Gomez-BoixLB18
fatcat:nn4g5of3hjb6jeeq7jsrczxnlu
Prolonging the Hide-and-Seek Game
2014
Proceedings of the 13th Workshop on Privacy in the Electronic Society - WPES '14
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By construction, our LPPMs take into account the sequential correlation across the user's exposed locations, providing the maximum possible trajectory privacy, i.e., privacy for the user's past, present ...
In this paper, we describe a method for creating LPPMs tailored to a user's mobility profile taking into her account privacy and quality of service requirements. ...
Hence, decisions taken to protect privacy (e.g., hiding, perturbing, or faking locations) need to be made locally to the users. ...
doi:10.1145/2665943.2665946
dblp:conf/wpes/Theodorakopoulos14
fatcat:kxgxn57745ehfh5gdzothkcjei
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2012.12803v2 fatcat:kakatsyvnfdinfifrxqynph3kiOther Versions
Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta [EFMRTT19] demonstrates that random shuffling amplifies differential privacy guarantees of locally randomized data. ...
As a direct corollary of our analysis we derive a simple and nearly optimal algorithm for frequency estimation in the shuffle model of privacy. ...
Introduction We consider privacy-preserving data analysis in the local model of differential privacy augmented with a shuffler. ...
arXiv:2012.12803v3
fatcat:zte5edcjnjcxtmljeoxnu3gg5m
Hiding in the Mobile Crowd: LocationPrivacy through Collaboration
2014
IEEE Transactions on Dependable and Secure Computing
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The results show that our scheme hides a high fraction of location-based queries, thus significantly enhancing users' location privacy. ...
We evaluate our scheme against Bayesian localization attacks, which allow for strong adversaries who can incorporate prior knowledge in their attacks. ...
Fig. 1 . 1 Users' hiding probability, due to MobiCrowd, for the region under study (in downtown San Francisco). ...
doi:10.1109/tdsc.2013.57
fatcat:mnfiqibf5jdejihzbnasa7yvcy
Hiding contextual information in WSNs
2012
2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)
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Compared to previous works, our method significantly reduces the communication overhead for hiding contextual information. ...
In our approach, we first reduce the number of bogus traffic sources necessary for hiding traffic patterns by finding minimum connected dominating sets that cover the deployment area. ...
ACKNOWLEDGMENTS This research was supported in part by NSF (under grants CNS-0844111, CNS-1016943, and CNS-1145913) Any opinions, findings, conclusions, or recommendations expressed in this paper are those ...
doi:10.1109/wowmom.2012.6263769
dblp:conf/wowmom/ProanoL12
fatcat:u6c7tazm5bbplfwzn7p7kfds2y
Differentially Private Matrix Factorization using Sketching Techniques
2016
Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security - IH&MMSec '16
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Differential privacy aims to minimize these identification risks by adding controlled noise with known characteristics. ...
We propose using sketching techniques to implicitly provide the differential privacy guarantees by taking advantage of the inherent randomness of the data structure. ...
[17] studied the theoretical properties of learning under differential privacy setup. ...
doi:10.1145/2909827.2930793
dblp:conf/ih/BaluF16
fatcat:uqecwcswancvrpklgf3uzuylhy
Formal approaches to information hiding: An analysis of interactive systems, statistical disclosure control, and refinement of specifications
[article]
2012
arXiv
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We focus on the concept of differential privacy, a notion that has become very popular in the database community. ...
We show how to model the query system in terms of an information-theoretic channel, and we compare the notion of differential privacy with that of min-entropy leakage.In the third scenario we address the ...
Deriving the relation between differential privacy and quantitative information flow on the basis of the graph structure and so they are all identical. ...
arXiv:1111.3013v3
fatcat:g65h6p4xsfe6xnxbhpwkksyjaq
InstaHide: Instance-hiding Schemes for Private Distributed Learning
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2010.02772v1 fatcat:f6dap2x6zben7pqlxpao5uivgy
How can multiple distributed entities collaboratively train a shared deep net on their private data while preserving privacy? ...
(b) Experimental results to show effectiveness in preserving privacy against known attacks with only minor effects on accuracy. ...
Differential privacy. Differential privacy for deep learning involves controlling privacy leakage by adding noise to the learning pipeline. ...
arXiv:2010.02772v2
fatcat:j37jyr4oy5gnpkdlhfuiqsmmvm
Information hiding-a survey
1999
Proceedings of the IEEE
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Information hiding techniques have recently become important in a number of application areas. ...
The first author is grateful to Intel Corporation for financial support under the grant 'Robustness of Information Hiding Systems' while the third author is supported by the European Commission under a ...
Since echo hiding gives best results for α greater than 0.7 we could useα -an estimator of α -drawn from, say a normal distribution centred on 0.8. ...
doi:10.1109/5.771065
fatcat:ncd7f24fvfdlvcruyr6u62zw2u
Hide & Share: Landmark-Based Similarity for Private KNN Computation
2015
2015 45th Annual IEEE/IFIP International Conference on Dependable Systems and Networks
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THE HIDE & SHARE LANDMARK-BASED SIMILARITY We address the privacy issues in decentralized KNN computation by introducing H&S (Hide & Share), a novel mechanism for similarity computation. ...
They also adapt elements of differential privacy so that the specific requirements of CF are met, thus retaining utility of recommendations. ...
doi:10.1109/dsn.2015.60
dblp:conf/dsn/FreyGKRTW15
fatcat:yb5udmb3crcl7hywzzbno4th3u
Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries
[article]
2018
arXiv
pre-print
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The level of privacy protection depends on a quantity related to the Laplacian matrix of the network graph and generally improves with the size of the graph. ...
of personal data collected in our everyday interactions with connected devices offers great opportunities for innovative services fueled by machine learning, as well as raises serious concerns for the privacy ...
A way to ensure privacy is that each user locally perturbs its own input before starting the algorithm so as to satisfy local differential privacy (Duchi et al., 2012; Kairouz et al., 2016) . ...
arXiv:1803.09984v1
fatcat:jjqbt36y6vaolp7zmlh4x6w67a
Hiding the Access Pattern is Not Enough: Exploiting Search Pattern Leakage in Searchable Encryption
[article]
2020
arXiv
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These findings highlight that hiding the search pattern, a feature that most constructions are lacking, is key towards providing practical privacy guarantees in SSE. ...
Our attack follows a maximum likelihood estimation approach, and is easy to adapt against SSE defenses that obfuscate the access pattern. ...
[4] propose a framework to hide access patterns in a differentially private way. ...
arXiv:2010.03465v1
fatcat:gf4bhzthhjblzgwfvbkmsgbmfe
Efficient Association Rules Hiding Using Genetic Algorithms
2018
Symmetry
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Sensitive association rules hiding (SARH) is an important goal of privacy protection algorithms. ...
Various approaches and algorithms have been developed for sensitive association rules hiding, differentiated according to their hiding performance through utility preservation, prevention of ghost rules ...
privacy breach. ...
doi:10.3390/sym10110576
fatcat:e4fl6x5m6fd2lhp2jl4bihj2na
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