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Towards Extending Noiseless Privacy – Dependent Data and More Practical Approach [article]

Krzysztof Grining, Marek Klonowski
2020 arXiv   pre-print
In our paper we extend the concept proposed by Bhaskar et al. and present some results for wider class of data. In particular we cover the data sets that are dependent.  ...  This requires non-asymptotic privacy guarantees, more realistic approach to the randomness inherently present in the data and to the adversary's knowledge.  ...  Showing precise bounds for privacy parameters and also considering dependent data is the way to make noiseless privacy more useful in practice, which is the purpose of our paper.  ... 
arXiv:1605.07956v5 fatcat:3mwrkdwbd5c7vlvi7jwuhl5dwu

Towards Extending Noiseless Privacy

Krzysztof Grining, Marek Klonowski
2017 Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security - ASIA CCS '17  
In our paper we extend the concept proposed by Bhaskar et al. and present some results for wider class of data. In particular we cover the data sets that are dependent.  ...  This requires non-asymptotic privacy guarantees, more realistic approach to the randomness inherently present in the data and to the adversary's knowledge.  ...  Showing precise bounds for privacy parameters and also considering dependent data is the way to make noiseless privacy more useful in practice, which is the purpose of our paper.  ... 
doi:10.1145/3052973.3052992 dblp:conf/ccs/GriningK17 fatcat:3d7tlhhivnaxxlnrp7mcd4kjca

Differential privacy with partial knowledge [article]

Damien Desfontaines, Esfandiar Mohammadi, Elisabeth Krahmer, David Basin
2020 arXiv   pre-print
We use these foundations to analyze practical scenarios: we significantly improve known results about the privacy of counting queries under partial knowledge, and we show that thresholding can provide  ...  Recent work has made significant steps towards privacy in the presence of partial background knowledge, which can model a realistic attacker's uncertainty.  ...  We propose a practical criterion and approach to fix this class of issues.  ... 
arXiv:1905.00650v6 fatcat:g52rue62cjhrjlol2l5f52qzj4

Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics [article]

Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton
2017 arXiv   pre-print
There are two inter-related issues for this disconnect between research and practice: (1) low model accuracy due to added noise to guarantee privacy, and (2) high development and runtime overhead of the  ...  While our approach trivially addresses (2), it makes (1) even more challenging.  ...  In the former case, more passes through the data introduces more noise due to privacy and thus results in worse test accuracy.  ... 
arXiv:1606.04722v3 fatcat:5pz2wqw5tbdzfgwlm7kek74zdi

XYZ Privacy [article]

Josh Joy, Dylan Gray, Ciaran McGoldrick, Mario Gerla
2018 arXiv   pre-print
The quality and usability of such privatized data will lie at the heart of future safe and efficient transportation solutions. In this paper, we present the XYZ Privacy mechanism.  ...  While vehicles are widely accepted as one of the most challenging mobility contexts in which to achieve effective data communications, less attention has been paid to the privacy of data emerging from  ...  We believe this represents an important and timely advance towards open and shared Internet of Vehicles data. X.  ... 
arXiv:1710.03322v5 fatcat:sxodr5x6bzhktezpus6plglnly

GenoPPML - a framework for genomic privacy-preserving machine learning [article]

Sergiu Carpov, Nicolas Gama, Mariya Georgieva, Dimitar Jetchev
2021 IACR Cryptology ePrint Archive  
We present a framework GenoPPML for privacy-preserving machine learning in the context of sensitive genomic data processing.  ...  place for both Tracks I and III of the genomic privacy competition iDASH'2020.  ...  Acknowledgement The results published here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.  ... 
dblp:journals/iacr/CarpovGGJ21 fatcat:yiglzptvnnc35lweezhu2lp4qa

Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics

Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey Naughton
2017 Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17  
There are two inter-related issues for this disconnect between research and practice: (1) low model accuracy due to added noise to guarantee privacy, and (2) high development and runtime overhead of the  ...  While our approach trivially addresses (2), it makes (1) even more challenging.  ...  Acknowledgments This work was supported by NSF under award 1253942 and by a gift from Google.  ... 
doi:10.1145/3035918.3064047 dblp:conf/sigmod/0001LKCJN17 fatcat:7nqou7wh4rfn5jrylmgc6olqu4

User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning

Dirk van der Hoeven
2019 Neural Information Processing Systems  
Local differential privacy is a strong notion of privacy in which the provider of the data guarantees privacy by perturbing the data with random noise.  ...  In the standard application of local differential privacy the distribution of the noise is constant and known by the learner.  ...  The new algorithms are a step towards practically useful algorithms with local differential privacy guarantees that have sound theoretical guarantees.  ... 
dblp:conf/nips/Hoeven19 fatcat:wufncad34ncghjfb6jskc7oxvm

IP Covert Channel Detection

Serdar Cabuk, Carla E. Brodley, Clay Shields
2009 ACM Transactions on Privacy and Security  
A network covert channel operates by altering the timing of otherwise legitimate network traffic so that the arrival times of packets encode confidential data that an attacker wants to exfiltrate from  ...  A covert channel can occur when an attacker finds and exploits a shared resource that is not designed to be a communication mechanism.  ...  data may be encrypted in order to add a further layer of privacy and obfuscation.  ... 
doi:10.1145/1513601.1513604 fatcat:irzzi4u2xjax3hx5gnaeuw2irq

The Roadmap to 6G Security and Privacy

Pawani Porambage, Gurkan Gur, Diana Pamela Moya Osorio, Madhusanka Liyanage, Andrei Gurtov, Mika Ylianttila
2021 IEEE Open Journal of the Communications Society  
All in all, this work intends to provide enlightening guidance for the subsequent research of 6G security and privacy at this initial phase of vision towards reality.  ...  Moreover, we discuss the security and privacy challenges that may encounter with the available 6G requirements and potential 6G applications.  ...  ACKNOWLEDGMENT This work has been performed under the framework of 6Genesis Flagship (grant 318927) and 5GEAR projects.  ... 
doi:10.1109/ojcoms.2021.3078081 fatcat:r5g662rcxjcgvjfc2el3lesdzy

Wireless Federated Langevin Monte Carlo: Repurposing Channel Noise for Bayesian Sampling and Privacy [article]

Dongzhu Liu, Osvaldo Simeone
2022 arXiv   pre-print
that is able to repurpose channel noise for the double role of seed randomness for MCMC sampling and of privacy preservation.  ...  To this end, based on the analysis of the Wasserstein distance between sample distribution and global posterior distribution under privacy and power constraints, we introduce a power allocation strategy  ...  We note that this result can be directly extended to device-dependent bounds k for each device k.  ... 
arXiv:2108.07644v2 fatcat:6c7jnuhevze3djnfb4fgcwtkh4

Distributed Hypothesis Testing Under Privacy Constraints

Sreejith Sreekumar, Deniz Gunduz, Asaf Cohen
2018 2018 IEEE Information Theory Workshop (ITW)  
However, this is seldom observed in practice, and often the data is observed remotely, and needs to be communicated to the detector over a noisy communication channel, such as a wired or a wireless communication  ...  The performance of a hypothesis test obviously depends on how accurately the observed data is communicated to the detector, i.e., less distortion of the data implies better performance.  ...  A rate-distortion approach to privacy is first explored by Yamamoto in [59] for a rate-constrained noiseless channel, where, in addition to a distortion constraint for legitimate data, a minimum distortion  ... 
doi:10.1109/itw.2018.8613433 dblp:conf/itw/SreekumarG018 fatcat:jewtqvjywjgbrk3sy3kd4wx66e

Testing Lipschitz Property over Product Distribution and its Applications to Statistical Data Privacy [article]

Kashyap Dixit and Madhav Jha and Abhradeep Thakurta
2012 arXiv   pre-print
In this work, we present a connection between Lipschitz property testing and a relaxed notion of differential privacy, where we assume that the datasets are being sampled from a domain according to some  ...  Acknowledgements: We would like to thank Sofya Raskhodnikova and Adam Smith for various suggestions and comments during the course of this project.  ...  Privacy: In this work, we took the first step towards designing efficient testing algorithm for statistical data privacy.  ... 
arXiv:1209.4056v1 fatcat:ot3q6x5lsnee5ibnyaepwrgojy

On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis [article]

James Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri
2016 arXiv   pre-print
We demonstrate the practicality of our approach on a time-series analysis of sensitive military records from the Afghanistan and Iraq wars disclosed by the Wikileaks organization.  ...  While this one posterior sample (OPS) approach elegantly provides privacy "for free," it is data inefficient in the sense of asymptotic relative efficiency (ARE).  ...  Chaudhuri and J. Geumlek was supported in part by NSF under IIS 1253942, and the work of M. Welling was supported in part by Qualcomm, Google and Facebook.  ... 
arXiv:1603.07294v2 fatcat:yrdgavap4radbjle5wojrevlra

Limiting Privacy Breaches in Average-Distance Query

Huihua Xia, Yan Xiong, Wenchao Huang, Zhaoyi Meng, Fuyou Miao
2020 Security and Communication Networks  
People are now suffering serious privacy leakage from various kinds of sources, especially service providers who provide insufficient protection on user's private data.  ...  We theoretically analyze how different factors affect the accuracy of the attack and propose the privacy-preserving mechanism based on the analysis.  ...  decrease towards the outskirts, and this comes from the reason that people are more active in the city center.  ... 
doi:10.1155/2020/8895281 fatcat:dzvtetxg7jbtrc72kipybocvcy
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