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Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data
[article]
2020
arXiv
pre-print
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We consider the heterogeneous data model, where different workers may have different local datasets, and we do not make any probabilistic assumptions on data generation. ...
In order to be able to apply their filtering procedure in our heterogeneous data setting where workers compute stochastic gradients, we derive a new matrix concentration result, which may be of independent ...
data. ...
arXiv:2005.07866v1
fatcat:ixlbiuqipfcztchkwaewrej77q
Analysis of Count Data by Transmuted Geometric Distribution
[article]
2016
arXiv
pre-print
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We have demonstrate two applications of (TGD) in modeling real life count data. ...
Data Analysis For the purpose of illustration, in this section, we consider following two data sets with details as follows: i. ...
Let the complete-data be constituted with observed set of values y = (y 1 , · · · , y n ) and the hypothetical data set x = (x 1 , · · · , x n ), where the observations y i 's are distributed with random ...
arXiv:1610.07123v1
fatcat:cgwxkxbl55a33jigdrkamzdxja
Data Encoding for Byzantine-Resilient Distributed Optimization
[article]
2020
arXiv
pre-print
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PGD is typically used in the data-parallel setting, where data is partitioned across different samples, whereas, CD is used in the model-parallelism setting, where data is partitioned across the parameter ...
We develop a sparse encoding scheme which enables computationally efficient data encoding and decoding. ...
Acknowledgements The work of Deepesh Data and Suhas Diggavi was partially supported by the Army Research Laboratory under Cooperative Agreement W911NF-17-2-0196, by the UC-NL grant LFR-18-548554, and by ...
arXiv:1907.02664v2
fatcat:ctptgj7gpfelraygvetdxrpy4m
Flexible Accuracy for Differential Privacy
[article]
2021
arXiv
pre-print
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Differential Privacy (DP) has become a gold standard in privacy-preserving data analysis. ...
In particular, we illustrate an application to differentially private histograms, which in turn yields mechanisms for revealing the support of a dataset or the extremal values in the data. ...
Acknowledgements The work of Deepesh Data was supported in part by NSF grants #1740047, #2007714, and UC-NL grant LFR-18-548554. ...
arXiv:2110.09580v1
fatcat:6hrekblz5bg2bfutupbwp5gcmi
Secure Computation of Randomized Functions: Further Results
[article]
2017
arXiv
pre-print
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Recently, Data (ISIT 2016) studied the remaining two cases (first, when privacy conditions are against both the users; and second, when privacy condition is only against Alice) and obtained single-letter ...
INTRODUCTION Two-user secure computation allows mutually distrusting users to jointly perform computation of their private data interactively, in such a way that no individual learns anything beyond the ...
Data [13] studied two variations of the same problem (one with privacy against both the users, and other with privacy only against Alice) with a probability distribution on inputs and gave rate-optimal ...
arXiv:1705.07081v1
fatcat:zuxpilcqk5e3bni2gs3b6bfrty
Successive Refinement of Privacy
[article]
2020
arXiv
pre-print
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We call this setting successive refinement of privacy, as it provides hierarchical access to the raw data with different privacy levels. ...
itself or to a function or statistic computed directly on the data. ...
This approach enables efficient hierarchical access to the data (for example, when analysts have different levels of authorized access). ...
arXiv:2005.11651v1
fatcat:eirqcetcwzcpdab4o7flyq4vhi
Byzantine-Resilient High-Dimensional Federated Learning
[article]
2020
arXiv
pre-print
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probabilistic assumptions on data generation. ...
We provide convergence analyses for strongly-convex and non-convex smooth objectives in the heterogeneous data setting, where different clients may have different local datasets, and we do not make any ...
., mobiles devices, organizations, etc.) collaboratively learn a machine learning model, where the training process is facilitated by the data held by the participating clients (without data centralization ...
arXiv:2006.13041v2
fatcat:ff7m23x625dqvlyv5kfpbqbjxq
A discrete probability model suitable for both symmetric and asymmetric count data
2020
Filomat
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In this paper, an alternative discrete probability model, namely the discrete skew logistic distribution, suitable for both asymmetric and symmetric count data is proposed. ...
Table 5 gives the statistical description of both the data sets and it is clear that coefficient of skewness for data set 1 is positive and for data set 2 is negative. ...
Skewness Kurtosis Data set 1
-2
0
0 0.421
1
3
1.067
0.299
3.4626
Data set 2
-3
1
3 2.625
4
7
2.562
-0.381
2.5921
Table 6 : 6 Data fitting for dataset 1 parameter(mse) DSL(µ, p, q) ...
doi:10.2298/fil2008559b
fatcat:x2d5dnxbnrh5vetqjbxr6oenra
Analysis of Count Data by Transmuted Geometric Distribution
2019
Journal of Statistical Theory and Applications (JSTA)
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We have demonstrate two applications of (TGD) in modeling real life count data. ...
DATA ANALYSIS For the purpose of illustration, in this section, we consider following two data sets with details as follows:
Number of Fires in Greece (NTG) The data comprise of numbers of fires in district ...
Insurance Claim Count (ICC) An insurance count data from Belgium in year 1993 is considered [13] and the data is as follows: 0(57178), 1(5617), 2(446), 3(50), 4(8). ...
doi:10.2991/jsta.d.191218.001
fatcat:wkyi55xcqvcxfmvx7ib4pwbu7i
A Uniform In Vitro Efficacy Dataset to Guide Antimicrobial Peptide Design
2019
Data
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Computational antimicrobial peptide design attempts typically use such data. However, utilizing heterogenous data aggregated from different sources presents significant drawbacks. ...
We draw simple inferences from this data, and we discuss what characteristics are essential for antimicrobial peptide efficacy. ...
Availability of CD data: Raw circular dichroism (CD) data has been made available in the supplementary information (Dataset S1). ...
doi:10.3390/data4010027
fatcat:liupru7dv5arbec4acahhpewvq
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning
[article]
2021
arXiv
pre-print
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We also demonstrate numerically significant improvement in privacy-learning performance operating point using real data sets. ...
Motivated by stochastic optimization and the federated learning (FL) paradigm, we focus on the case where a small fraction of data samples are randomly sub-sampled in each round to participate in the learning ...
This is because no client has access to enough data to build rich learning models locally and we do not want to directly share local data. ...
arXiv:2107.08763v1
fatcat:nqm5kksvdncqjeupmkszhib22u
On the Renyi Differential Privacy of the Shuffle Model
[article]
2021
arXiv
pre-print
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Introduction Differential privacy (DP) [DMNS06] gives a principled and rigorous framework for data privacy by giving guarantees on the information leakage for individual data points from the output of ...
Problem Formulation Let D = (d 1 , . . . , d n ) be a dataset consisting of n data points, where d i is a data point at the i'th client that takes values from a set X . ...
arXiv:2105.05180v1
fatcat:f7ncpfe34jcqff5k6q464xiham
On the Communication Complexity of Secure Computation
[article]
2014
arXiv
pre-print
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Our techniques include the use of a data processing inequality for residual information - i.e., the gap between mutual information and G\'acs-K\"orner common information, a new information inequality for ...
Similarly, the terms in (17) are evaluated using the the distribution p X of the data X of Alice. The lower bound in (18) does not depend on the distributions p X and p Y of the data. ...
In [WW08], Wolf and Wullschleger identified (among other things) the following important data processing inequality for residual information. ...
arXiv:1311.7584v2
fatcat:ksgnrmbq7zeu3ndj5v63bshcma
Interactive Secure Function Computation
[article]
2020
arXiv
pre-print
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Data the input distribution and q Z1Z2|XY specifies the randomized function. ...
By the data-processing inequality, this minimization can be restricted to W 's ranging over maximal independent sets of G [19] . ...
arXiv:1812.03838v3
fatcat:4sphlriuljb4dn6whi5oxinn7i
On coding for secure computing
2015
2015 IEEE International Symposium on Information Theory (ISIT)
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about other users' data other than what they can infer from their own data and the function value they compute. ...
In information theoretically secure multiparty computation, several mutually distrusting users want to jointly compute a function of their private data such that users do not learn any additional information ...
private data to each other. ...
doi:10.1109/isit.2015.7282954
dblp:conf/isit/DataP15
fatcat:o7l5jduqobaaxevgx4ea7xv2kq
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