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Scalable Privacy-Preserving Distributed Learning [article]

David Froelicher, Juan R. Troncoso-Pastoriza, Apostolos Pyrgelis, Sinem Sav, Joao Sa Sousa, Jean-Philippe Bossuat, Jean-Pierre Hubaux
2021 arXiv   pre-print
We design SPINDLE (Scalable Privacy-preservINg Distributed LEarning), the first distributed and privacy-preserving system that covers the complete ML workflow by enabling the execution of a cooperative  ...  In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstraction that we extend with  ...  RELATED WORK Privacy-Preserving Training of Machine Learning Models.  ... 
arXiv:2005.09532v5 fatcat:lejdza4ku5hxnfi6rqomdepzpa

COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework [article]

Anwaar Ulhaq, Oliver Burmeister
2020 arXiv   pre-print
with scalability and robustness.  ...  We argue that scalable differentially private federated learning design is a promising solution for building a secure, private and collaborative machine learning model such as required to combat COVID19  ...  The proposed Differential Privacy by Design (dPbD) framework In privacy-preserving machine learning, we search for an algorithm that takes as input a dataset (sampled from some distribution), and then  ... 
arXiv:2010.06177v1 fatcat:asfm6ub2krc7bj3p2vyugqfdiu

A systematic review on privacy-preserving distributed data mining

Chang Sun, Lianne Ippel, Andre Dekker, Michel Dumontier, Johan van Soest, Karin Verspoor
2021 Data Science  
Privacy-preserving distributed data mining techniques (PPDDM) aim to overcome these challenges by extracting knowledge from partitioned data while minimizing the release of sensitive information.  ...  We discuss the ambiguous definitions of privacy and confusion between privacy and security in the field, and provide suggestions of how to make a clear and applicable privacy description for new PPDDM  ...  In a recent survey [77] , privacy-preserving approaches were summarized for data collection, data publishing, data mining output, and distributed learning.  ... 
doi:10.3233/ds-210036 fatcat:tzdj4mggv5f6xld3tvpghgltd4

Privacy Preserving Combinatorial Function for Multi-Partitioned Data Sets

V. S.Prakash, A. Shanmugam, P. Murugesan
2012 International Journal of Computer Applications  
To facilitate privacy preservation in data mining or machine learning algorithms over horizontally partitioned or vertically partitioned data, many protocols have been proposed using SMC and various secure  ...  In this work, we plan to present an effective and efficient cluster based privacy preserving data perturbation technique to mine Multi-partitioned data sets that comprises of both vertical and horizontal  ...  In [12] proposed privacy preservation for the same sets but with distributed sets of data. The authenticity of the data may be lost here due to the distributed approach.  ... 
doi:10.5120/6281-8450 fatcat:u6hlf2f2kzbqrlp2tz53d3qavu

k-TTP

Bobi Gilburd, Assaf Schuster, Ran Wolff
2004 Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04  
This allows implementing cryptographically secure efficient primitives for real-world large-scale distributed systems.  ...  , while ensuring that the privacy is cryptographically secure.  ...  k-TTP defined as above preserves k-privacy.  ... 
doi:10.1145/1014052.1014120 dblp:conf/kdd/GilburdSW04 fatcat:45qlqyig6zghzijcskzadm2urq

Privacy-preserving Artificial Intelligence Techniques in Biomedicine [article]

Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B. Blumenthal, Tim Kacprowski, Markus List, Julian Matschinske, Julian Späth, Nina Kerstin Wenke, Béla Bihari, Tobias Frisch, Anne Hartebrodt, Anne-Christin Hausschild (+13 others)
2020 arXiv   pre-print
As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy preserving techniques.  ...  This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications.  ...  is the major challenge to scalability of federated learning.  ... 
arXiv:2007.11621v2 fatcat:qnmzqvqn5fgonjiwmudjlzwelm

Enforcing Secure and Privacy-Preserving Information Brokering in Distributed Information Sharing

Vrushali Ranmalkar
2018 International Journal for Research in Applied Science and Engineering Technology  
To preserve privacy of multiple stakeholders involved, in this information brokering process. A selected set of brokering servers, to securely share the routing decision making responsibility among.  ...  From metadata exchanged within the IBS, privacy of data location and data consumer can still be inferred, but little attention has been put on his protection.  ...  In many data mining applications in distributed environment, privacy is becoming an increasingly important issue. To solve this problem, privacy preserving data mining technique gives new direction.  ... 
doi:10.22214/ijraset.2018.5108 fatcat:2ne23pslxvekdj7y4lyco4fa5u

Guest Editorial Introduction to the Special Section on Scalability and Privacy in Social Networks

Donghyun Kim, My T. Thai, R. N. Uma
2020 IEEE Transactions on Network Science and Engineering  
In "Scalable Privacy-Preserving Participant Selection for Mobile Crowdsensing Systems: Participant Grouping and Secure Group Bidding," Li et al. study how to protect the temporal and spatial privacies  ...  In this work, they carefully design a scalable grouping-based privacy-preserving participant selection scheme, where participants are grouped into multiple participant groups and then auctions are organized  ... 
doi:10.1109/tnse.2019.2959674 fatcat:pm5hzj4pczddhmjlucpxhk75ey

Special issue on in-database analytics

Dan Olteanu, Florin Rusu
2017 Distributed and parallel databases  
The topics of the articles cover in-database linear algebra algorithms for massive graphs, privacy-preserving classification with missing data, parallel skyline computation on MapReduce, and indexing in  ...  As a result, many libraries, frameworks, and platforms have been recently developed to provide scalable support for distributed and parallel statistical analytics.  ...  Mohamed Mokbel, the Editors-in-Chief of DAPD, for their strong support for this special issue. • Privacy-preserving of SVM over vertically partitioned with imputing missing data by Mohammed Z.  ... 
doi:10.1007/s10619-017-7204-2 fatcat:cvll3nwjereirluvgztumppute

Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices

Jihun Hamm, Adam C. Champion, Guoxing Chen, Mikhail Belkin, Dong Xuan
2015 2015 IEEE 35th International Conference on Distributed Computing Systems  
This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowdsensing data with differential privacy  ...  We analyze the performance and the scalability of Crowd-ML, and implement the system with off-the-shelf smartphones as a proof of concept.  ...  Differential privacy has been used for privacy-preserving data analysis platform [28] , for sanitization of learned models parameters from data [29] , and for privacypreserving data mining from distributed  ... 
doi:10.1109/icdcs.2015.10 dblp:conf/icdcs/HammCCBX15 fatcat:weclmig43nc7dabr27k3wakihi

Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms [article]

Joohyung Jeon, Junhui Kim, Joongheon Kim, Kwangsoo Kim, Aziz Mohaisen,, Jong-Kook Kim
2020 arXiv   pre-print
This paper proposes a distributed deep learning framework for privacy-preserving medical data training.  ...  Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during  ...  SUMMARY AND FUTURE WORK In this paper, we propose a distributed deep learning framework for privacy-preserving computation.  ... 
arXiv:2001.02932v1 fatcat:fxkwzsucajbrdntdootntecjgq

An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems [article]

Yu Xianjia, Jorge Peña Queralta, Jukka Heikkonen, Tomi Westerlund
2021 arXiv   pre-print
Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates.  ...  While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration.  ...  In consequence, multiple research directions have emerged to make distributed learning processes more scalable, secure and privacy-preserving through [28] [29] [30] [31] [32] .  ... 
arXiv:2104.10141v2 fatcat:x4mysoxyzzagpjxpzmpxu2af4q

Enforcing Secure and Privacy-Preserving Information Brokering in Distributed Information Sharing

Swati Somavanshi
2018 International Journal for Research in Applied Science and Engineering Technology  
To preserve privacy of multiple stakeholders involved, in the information brokering process in this paper, we propose a novel approach.  ...  From metadata exchanged within the IBSs, privacy of data location and data consumer can still be inferred, but little attention has been put on its protection.  ...  Privacy is becoming an increasingly important issue in many data mining applications in distributed environment. Privacy preserving data mining technique gives new direction to solve this problem.  ... 
doi:10.22214/ijraset.2018.5271 fatcat:p6p33rlbgvcvpeaxmy4y23ce44

Table of Contents

2021 2021 IEEE 46th Conference on Local Computer Networks (LCN)  
and Privacy-Preserving Continuous Authentication for Web Applications 281 Protecting Software-Defined Enterprise Networks from Packet Injection Attacks 287 Intelligent Cooperative Health Emergency Response  ...  Networks 355 An Incentive Approach in Mobile Crowdsensing for Perceptual User 359 A Smartphone-Targeted Opportunistic Computing Environment for Decentralized Web Applications 363 LaFlector: A Privacy-Preserving  ... 
doi:10.1109/lcn52139.2021.9524933 fatcat:bopsc4l2qrc7bobzfyb6343iou

Privacy Preserving Data Mining Techniques in a Distributed Environment

Mona Shah, Hiren D. Joshi
2014 International Journal of Computer Applications  
El Abbadi, "Privacy preserving decision tree learning over multiple parties" Using ID3 algorithm over multiple parties-a scalable secured distributed ID3 for building a decision tree.  ...  Wright, Member IEEE, "Privacy- Preserving Computation of Bayesian Networks on Vertically Partitioned Data" Secure distributed computation with Bayesian network Parties do not learn about individual  ... 
doi:10.5120/16347-5687 fatcat:odk4hyhdunfvneimuvvprvfo7u
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