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Optimal Accuracy-Privacy Trade-Off for Secure Multi-Party Computations
[article]
2018
arXiv
pre-print
Preserved Fulltext
Finally, we investigate the inherent compromise between accuracy of computation and privacy of inputs and we demonstrate how to realise such optimal trade-offs. ...
The purpose of Secure Multi-Party Computation is to enable protocol participants to compute a public function of their private inputs while keeping their inputs secret, without resorting to any trusted ...
This presents an inherent trade-off between the accuracy of the output and the privacy of the inputs. We will understand this trade-off in detail in Section 8. ...
arXiv:1803.00436v1
fatcat:rofor4av2veihcv2lt75jpzpbq
Achieving Secure and Differentially Private Computations in Multiparty Settings
[article]
2017
arXiv
pre-print
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Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed parties to make computations while the parties learn nothing about their data, but the final result. ...
The protocol implements homomorphic encryption for SMC and functional mechanism for DP to achieve the desired security and privacy guarantees. ...
Accuracy Analysis We evaluate the accuracy-privacy trade-off of distributed evaluation of differential privacy on linear regression. Specif- Figure 6 . ...
arXiv:1707.01871v1
fatcat:bcwxjj2yg5amdpnhd225t6igke
Privacy-Preserving Federated Neural Network Learning for Disease-Associated Cell Classification
[article]
2022
bioRxiv
pre-print
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Our work guarantees patient privacy and ensures data utility for efficient multi-center studies involving complex healthcare data. ...
Our solution achieves an accuracy comparable to the one obtained with the centralized solution, with an improvement of at least one-order-of-magnitude in execution time with respect to prior secure solutions ...
We also thank Shufan Wang and Joao Sa Sousa for their contribution on the experiments and benchmarking. ...
doi:10.1101/2022.01.10.475610
fatcat:uokre4qusnfmbbzaqzrmb6in5e
DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications
[article]
2020
arXiv
pre-print
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First, we survey solutions that combine cryptographic primitives like secure computation and anonymous communication with differential privacy to give alternatives to the LDP model that avoid a trusted ...
server as in SDP but close the gap in accuracy. ...
On the other hand, systems such as RAPPOR allow for approximate computation of statistics and primarily provide a privacy-utility trade-off. ...
arXiv:2004.08887v1
fatcat:xkwpazhrd5gyndl2sbs3pquxni
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications
[article]
2020
arXiv
pre-print
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This could produce more accurate models for important tasks like optimal trade execution, credit origination, or fraud detection. ...
A system can further reduce the coordinating server's ability to recover private client information, without additional accuracy loss, by also including secure multiparty computation. ...
Morgan makes no representation and warranty whatsoever and disclaims all liability, for the completeness, accuracy or reliability of the information contained herein. ...
arXiv:2010.05867v1
fatcat:gtrmrbyeqbcltmcq2viruifezy
Secure Multi-party Computation of Differentially Private Median
2020
USENIX Security Symposium
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data universe (secure multi-party computation). ...
) and convex optimizations for machine learning. ...
Preliminaries In the following, we introduce preliminaries for differential privacy and secure multi-party computation. ...
dblp:conf/uss/BohlerK20
fatcat:iesp7lhvyjabvjqsiygjrw6z2m
Mining multiple private databases using a kNN classifier
2007
Proceedings of the 2007 ACM symposium on Applied computing - SAC '07
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A salient feature of our approach is that it offers a trade-off between accuracy, efficiency and privacy through multi-round protocols. ...
Modern electronic communication has collapsed geographical boundaries for global information sharing but often at the expense of data security and privacy boundaries. ...
At the other end, we have the secure multi-party computation protocols, using which we can construct classifiers which are provably secure in the sense that they reveal the least amount of information ...
doi:10.1145/1244002.1244102
dblp:conf/sac/XiongCL07
fatcat:th4esaqpf5b3lcozc5dlegrfwi
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
2018
Neural Information Processing Systems
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For both methods, we show that the noise can be reduced in the multi-party setting by adding the noise inside the secure computation after aggregation, asymptotically improving upon the best previous results ...
In our output perturbation method, the parties combine local models within a secure computation and then add the required differential privacy noise before revealing the model. ...
Background on Differential Privacy and Multi-Party Computation This section introduces differential privacy (including the zero-concentrated differential privacy notion we use), and secure multi-party ...
dblp:conf/nips/JayaramanW0G18
fatcat:smqfiv7gvzdy7kstfbg6vl5wam
Differentially-Private Multi-Party Sketching for Large-Scale Statistics
2020
Proceedings on Privacy Enhancing Technologies
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among efficiency, privacy and accuracy of our implementation for varying numbers of parties and input sizes.Finally, we generalize our approach for the LogLog sketch and obtain a general framework for ...
constructing multi-party differentially private protocols for several other sketching algorithms. ...
All DP mechanisms inherently suffer from a trade-off between accuracy and privacy. ...
doi:10.2478/popets-2020-0047
fatcat:tuadgjwozrfrtdcgmvocxmqsnq
Towards Privacy-Preserving Neural Architecture Search
[article]
2022
arXiv
pre-print
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To address these issues, we propose a privacy-preserving neural architecture search (PP-NAS) framework based on secure multi-party computation to protect users' data and the model's parameters/hyper-parameters ...
Extensive analyses and experiments demonstrate PP-NAS's superiority in security, efficiency, and accuracy. ...
To protect user privacy, privacy-preserving machine learning (PPML) based on secure multi-party computation (MPC) [6] - [9] has been proposed and implemented. ...
arXiv:2204.10958v1
fatcat:ta7neeob4newdgridsdvcxz73i
SAQE: Practical Privacy-Preserving Approximate Query Processing for Data Federations
2020
Proceedings of the VLDB Endowment
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Experimentally, we show that this result enables SAQE to trade-off among these three criteria to scale its query processing to very large datasets with accuracy bounds dependent only on sample size, and ...
, secure computation, and approximate query processing -in a novel and principled way. ...
Finally, we examine the effectiveness of our cost model and optimization in relation to the trade-offs between accuracy, privacy, and efficiency. ...
dblp:journals/pvldb/BaterP0WR20
fatcat:sfg3d4wkg5h2jfpwfhz272m364
Enhancing the Privacy of Federated Learning with Sketching
[article]
2019
arXiv
pre-print
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To better optimize these trade-offs, we propose that sketching algorithms have a unique advantage in that they can provide both privacy and performance benefits while maintaining accuracy. ...
Our initial findings show that it is possible to provide strong privacy guarantees for federated learning without sacrificing performance or accuracy. ...
For instance, [10] brings secure multi-party computation (SMC) into federated learning. ...
arXiv:1911.01812v1
fatcat:mquqgd2ykjepdidtf5bx4rkkpq
Modeling Privacy Aware Information Sharing Systems: A Formal and General Approach
2016
2016 IEEE Trustcom/BigDataSE/ISPA
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, and includes a novel method to compute the trade-off between privacy and accuracy. ...
This work also proposes a methodology to choose, case-by-case, the privacy mechanism which maximizes the trade-off between privacy and accuracy. ...
To preserve privacy, in this architecture information is shared and analysis is performed by mean of secure multi-party computation algorithms, mainly based on homomorphic encryption, which allows data ...
doi:10.1109/trustcom.2016.0137
dblp:conf/trustcom/MartinelliSS16
fatcat:o7avy2nxobbl5fuxtht3rad7nu
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
[article]
2021
arXiv
pre-print
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Hence, well-designed privacy-preserving ML (PPML) solutions are critically needed for many emerging applications. ...
Usually, a well-performing ML model relies on a large volume of training data and high-powered computational resources. ...
-public key infrastructure; SS -secret sharing; 2PC -secure two-party computation;
MPC -secure multi-party computation. ...
arXiv:2108.04417v2
fatcat:pmxmsbs2gvh6nd4jadcz4dnsrq
Privacy Preserving Image Registration
[article]
2022
arXiv
pre-print
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We derive our privacy preserving image registration framework by extending classical registration paradigms to account for advanced cryptographic tools, such as secure multi-party computation and homomorphic ...
We demonstrate our privacy preserving framework in linear and non-linear registration problems, evaluating its accuracy and scalability with respect to standard image registration. ...
Secure Computation Secure Multi-Party Computation. ...
arXiv:2205.10120v4
fatcat:naalcnlbqrb3lj3wbnrl2qr52u
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