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Differentially Private Bayesian Programming

Gilles Barthe, Gian Pietro Farina, Marco Gaboardi, Emilio Jesus Gallego Arias, Andy Gordon, Justin Hsu, Pierre-Yves Strub
2016 Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS'16  
We present PrivInfer, an expressive framework for writing and verifying differentially private Bayesian machine learning algorithms.  ...  We demonstrate the expressiveness of PrivInfer by verifying privacy for several examples of private Bayesian inference.  ...  Let M : D Ñ DpRq be an p , δq-differentially private program. Let N : R Ñ DpR 1 q be an arbitrary randomized program. Then λd.bind pM dq N : D Ñ DpR 1 q is p , δq-differentially private.  ... 
doi:10.1145/2976749.2978371 dblp:conf/ccs/BartheFGAGHS16 fatcat:lrfmbhtqvbfhbm3bwafgdlpvmu

Bayesian analysis of the genetic structure of a Brazilian popcorn germplasm using data from simple sequence repeats (SSR)

Javier Saavedra¹, Tereza Aparecida Silva, Freddy Mora, Carlos Alberto Scapim
2013 Chilean Journal of Agricultural Research  
A Bayesian clustering approach via Monte Carlo Markov chains was performed to examine the genetic differentiation (FST values) among different clusters.  ...  The results indicate the existence of three distinct and strongly differentiated genetic groups (K = 3).  ...  Additionally, we emphasize the Bayesian paradigm as an important issue to keep in mind in future studies related to genetic differentiation of popcorn germplasms. Table 6 .  ... 
doi:10.4067/s0718-58392013000200003 fatcat:sglwkkgtzreu3hgdgmjsjqb7na

High genetic diversity and connectivity in Colossoma macropomum in the Amazon basin revealed by microsatellite markers

Paola Fazzi-Gomes, Sávio Guerreiro, Glauber David Almeida Palheta, Nuno Filipe Alves Correa de Melo, Sidney Santos, Igor Hamoy
2017 Genetics and Molecular Biology  
The Bayesian population clustering analysis indicated a single population.  ...  This information is important for programs aiming at the conservation of natural populations.  ...  This program uses Bayesian analysis to infer the number of genetically homogeneous populations (K) most likely (mean of Ln prob) to occur in the database analyzed.  ... 
doi:10.1590/1678-4685-gmb-2015-0222 pmid:28170026 pmcid:PMC5409762 fatcat:vxiivkc7kvcvxpxnhng3axgwn4

Estimation Bias in Complete-Case Analysis in Crossover Studies with Missing Data

Fang Liu
2011 Communications in Statistics - Theory and Methods  
A Bayesian fill-in approach for adjusting for publication bias in meta-analysis, International Chinese  ...  Liu, F. (2016), Model-based Differentially Private Data Synthesis (arXiv:1606.08052) 49.  ...  Liu, F. (2016), Generalized Gaussian Mechanism in Differential Privacy (arXiv:1602.06028) 48. # Bowen, C., Liu, F. (2016), Comparative Study of Differentially Private Data Synthesis Methods (arXiv:1602.01063  ... 
doi:10.1080/03610920903427800 fatcat:5cyubqbwejat5h6znlvknxhsra

Efficient Hyperparameter Optimization for Differentially Private Deep Learning [article]

Aman Priyanshu, Rakshit Naidu, Fatemehsadat Mireshghallah, Mohammad Malekzadeh
2021 arXiv   pre-print
Tuning the hyperparameters in the differentially private stochastic gradient descent (DPSGD) is a fundamental challenge.  ...  general optimization framework for establishing a desirable privacy-utility tradeoff, and systematically study three cost-effective algorithms for being used in the proposed framework: evolutionary, Bayesian  ...  EP/T023600/1) within the CHIST-ERA program.  ... 
arXiv:2108.03888v1 fatcat:gomewnongbei3dptgmyniiniz4

Genetic Heterogeneity in a Cyclical Forest Pest, the Southern Pine Beetle,Dendroctonus frontalis, is Differentiated Into East and West Groups in the Southeastern United States

Natalie M Schrey, Aaron W. Schrey, Edward J. Heist, John D. Reeve
2011 Journal of Insect Science  
Bayesian clustering identified east and west genetic groups spanning multiple states.  ...  Significant genetic differentiation (θ ST = 0.01, p < 0.0001) followed an isolation-by-distance pattern (r = 0.39, p < 0.001) among samples, and a hierarchical AMOVA indicated slightly more differentiation  ...  Figure 2 . 2 Bayesian clustering of southern pine beetles with the program TESS. Results are provided for 100 runs at k=2 summarized with CLUMPP.  ... 
doi:10.1673/031.011.11001 pmid:22220595 pmcid:PMC3281376 fatcat:jcq72a4wyjhpnn7oayjzaju4cq

Impossibility of Differentially Private Universally Optimal Mechanisms [article]

Hai Brenner, Kobbi Nissim
2010 arXiv   pre-print
Ghosh et al. have demonstrated, quite surprisingly, a case where such a universally-optimal differentially-private mechanisms exists, when the information consumers are Bayesian.  ...  We show, however, that universally-optimal mechanisms do not exist for all these queries, both for Bayesian and risk-averse consumers.  ...  Let X be an α-differentially private mechanism. X is maximally general if for every α-differentially private mechanism Y , if X G Y then Y G X.  ... 
arXiv:1008.0256v1 fatcat:hbngpniqjfdgnjmroyrtywstl4

Grafting versus seed propagated apricot populations: two main gene pools in Tunisia evidenced by SSR markers and model-based Bayesian clustering

Hedia Bourguiba, Bouchaib Khadari, Lamia Krichen, Neila Trifi-Farah, Sylvain Santoni, Jean-Marc Audergon
2010 Genetica  
The model-based Bayesian clustering analysis using both Structure and InStruct programs as well as the multivariate method revealed five distinct genetic clusters.  ...  The genetic differentiation among clusters showed that cluster 1, with only four cultivars, was the most differentiated from the four remaining genetic clusters, which constituted the largest part of the  ...  Genetic differentiation Based on the five clusters defined by the model-based Bayesian clustering algorithm using STRUCTURE program, we observed high significant genetic differentiation ranged from Fst  ... 
doi:10.1007/s10709-010-9488-2 pmid:20838857 pmcid:PMC2948653 fatcat:eb5p3yurifdn7ksv77hwkaumry


Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, Xiaokui Xiao
2014 Proceedings of the 2014 ACM SIGMOD international conference on Management of data - SIGMOD '14  
To address the deficiency of the existing methods, this paper presents PRIVBAYES, a differentially private method for releasing high-dimensional data.  ...  Private construction of Bayesian networks turns out to be significantly challenging, and we introduce a novel approach that uses a surrogate function for mutual information to build the model more accurately  ...  PRIVBAYES satisfies ε-differential privacy. PRIVATE BAYESIAN NETWORKS This section presents our solution for constructing differentially private Bayesian networks.  ... 
doi:10.1145/2588555.2588573 dblp:conf/sigmod/ZhangCPSX14 fatcat:fvymy4yoczbnpj6pdauotjipda

Co-clustering for Differentially Private Synthetic Data Generation [chapter]

Tarek Benkhelif, Françoise Fessant, Fabrice Clérot, Guillaume Raschia
2017 Lecture Notes in Computer Science  
The sequence is therefore ε-differentially private with ε = ε 1 + ε 2 .  ...  In this paper, we study the problem of differentially private data generation.  ... 
doi:10.1007/978-3-319-71970-2_5 fatcat:vavkw5q7znfurjdix6c5vobxji

Privacy Games: Optimal User-Centric Data Obfuscation

Reza Shokri
2015 Proceedings on Privacy Enhancing Technologies  
Their utility cost is also not larger than what either of the differential or distortion mechanisms imposes.  ...  We optimize utility subject to a joint guarantee of differential privacy (indistinguishability) and distortion privacy (inference error).  ...  The most related paper to our framework, in this domain, is [9] where the authors construct utilitymaximizing differentially private obfuscation mechanisms using linear programming.  ... 
doi:10.1515/popets-2015-0024 dblp:journals/popets/Shokri15 fatcat:dzulb6iqkva3faphtr73ilwr5q

Gene variation and genetic differentiation among populations of the solitary mud dauber wasp Trypoxylon (Trypargilum) albitarse Fabricius 1804 (Hymenoptera, Crabronidae)

Antonio C.B. Bergamaschi, Marco A. Del Lama
2015 Genetics and Molecular Biology  
The analysis of allelic richness and private alleles indicated high genetic diversity in the populations sampled.  ...  Additionally, the analysis of population structure using Bayesian and PCA methods characterized two alternative genetic groups.  ...  Despite the similar heterozygosities, the number of private alleles at each locus differed (Table 2) , suggesting inter-population differentiation.  ... 
doi:10.1590/s1415-475738420150097 pmid:26692160 pmcid:PMC4763321 fatcat:uykpbac5rzf7pahh4lxrijdgn4

Pain-Free Random Differential Privacy with Sensitivity Sampling [article]

Benjamin I. P. Rubinstein, Francesco Aldà
2017 arXiv   pre-print
computer programs.  ...  Since our sensitivity estimates hold with high probability, any mechanism that would be (ϵ,δ)-differentially private under bounded global sensitivity automatically achieves (ϵ,δ,γ)-random differential  ...  Combined with generic mechanisms like Laplace, exponential, Gaussian or Bernstein, our sampler enables systematising of privatisation: arbitrary computer programs can be made differentially private with  ... 
arXiv:1706.02562v1 fatcat:ah3vks2ymjh2lcxsvhfcfwl55m

2020 CI Year End Index

2020 IEEE Computational Intelligence Magazine  
Takahashi, C., +, MCI Aug. 2020 16-27 A Survey on Differentially Private Machine Learning [Review Article]. Survey on Differentially Private Machine Learning [Review Article].  ...  Qureshi, S., +, MCI Aug. 2020 47-59 Differential privacy A Survey on Differentially Private Machine Learning [Review Article].  ... 
doi:10.1109/mci.2020.3035941 fatcat:dd25jk7yxrfmlh7f6ljueejv7m

Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost? [article]

Debabrota Basu, Christos Dimitrakakis, Aristide Tossou
2020 arXiv   pre-print
We observe that the dependency is weaker when we do not require local differential privacy for the rewards.  ...  Based on differential privacy (DP) framework, we introduce and unify privacy definitions for the multi-armed bandit algorithms.  ...  Acknowledgement This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.  ... 
arXiv:1905.12298v2 fatcat:eiv2s4b7yrgajjr2vl3m6z2udi
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