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SoK: Privacy-Preserving Collaborative Tree-based Model Learning [article]

Sylvain Chatel, Apostolos Pyrgelis, Juan Ramon Troncoso-Pastoriza, Jean-Pierre Hubaux
2021 arXiv   pre-print
In this work, we survey the literature on distributed and privacy-preserving training of tree-based models and we systematize its knowledge based on four axes: the learning algorithm, the collaborative  ...  The recent orthogonal needs for more data and privacy protection call for collaborative privacy-preserving solutions.  ...  THREAT MODEL We now systematize existing works on privacy-preserving collaborative decision-tree learning based on their threat model (see Table III ).  ... 
arXiv:2103.08987v2 fatcat:3c6axws5zbeptn5q3bjomusbg4

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

Sylvain Chatel, Apostolos Pyrgelis, Juan Ramón Troncoso-Pastoriza, Jean-Pierre Hubaux
2021 Proceedings on Privacy Enhancing Technologies  
In this work, we survey the literature on distributed and privacy-preserving training of tree-based models and we systematize its knowledge based on four axes: the learning algorithm, the collaborative  ...  The recent orthogonal needs for more data and privacy protection call for collaborative privacy-preserving solutions.  ...  Collaborative Model We propose a systematization of the literature on privacy-preserving collaborative tree-based model learning based on their collaborative model.  ... 
doi:10.2478/popets-2021-0043 fatcat:zjlt2fgmpfcw7modhaddyqc46i

SoK: Efficient Privacy-preserving Clustering

Aditya Hegde, Helen Möllering, Thomas Schneider, Hossein Yalame
2021 Proceedings on Privacy Enhancing Technologies  
This calls for efficient privacy-preserving clustering. In this work, we systematically analyze the state-of-the-art in privacy-preserving clustering.  ...  Clustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. It is used in many areas ranging from business analysis to health care.  ...  It was cofunded by the Deutsche Forschungsgemeinschaft (DFG) -SFB 1119 CROSSING/236615297 and GRK 2050 Privacy & Trust/251805230, and by the BMBF and the HMWK within ATHENE.  ... 
doi:10.2478/popets-2021-0068 fatcat:sb2ttfjfojb45lkudipt5tu4fq

SoK: Machine Learning Governance [article]

Varun Chandrasekaran, Hengrui Jia, Anvith Thudi, Adelin Travers, Mohammad Yaghini, Nicolas Papernot
2021 arXiv   pre-print
The application of machine learning (ML) in computer systems introduces not only many benefits but also risks to society.  ...  Our approach first systematizes research towards ascertaining ownership of data and models, thus fostering a notion of identity specific to ML systems.  ...  Privacy: Several approaches can be taken to preserve data privacy. The de-facto approach is to utilize DP learning. Proposed initially by Chaudhuri et al.  ... 
arXiv:2109.10870v1 fatcat:7zklvf3ocjeaje6pq45cgp4zkm

SoK: Cryptographically Protected Database Search

Benjamin Fuller, Mayank Varia, Arkady Yerukhimovich, Emily Shen, Ariel Hamlin, Vijay Gadepally, Richard Shay, John Darby Mitchell, Robert K. Cunningham
2017 2017 IEEE Symposium on Security and Privacy (SP)  
these base operations.  ...  . • A querier, who wishes to learn things about the data. • A server, who handles storage and processing.  ...  Chase and Shen [109] design a protection method based on suffix trees to enable substring search. Tree-based indices are also amenable to range searches.  ... 
doi:10.1109/sp.2017.10 dblp:conf/sp/FullerVYSHGSMC17 fatcat:mnig3cecgjg5jiwpdwmx736uye

SoK: Cryptojacking Malware [article]

Ege Tekiner, Abbas Acar, A. Selcuk Uluagac, Engin Kirda, Ali Aydin Selcuk
2021 arXiv   pre-print
Emerging blockchain and cryptocurrency-based technologies are redefining the way we conduct business in cyberspace.  ...  : RNN, Incremental Learning: IL, Threshold-based: Thr-based, Manual Analysis: MA, Dendritic Cell Algorithm: DCA, k-Nearest Neighbors: k-NN, Light-weight machine learning models: LSTM, Symantec RuleSpace  ...  Then, the system calls are used to train deep learning models, and they achieve 99% accuracy. 6.1.4. Classifier and Performance.  ... 
arXiv:2103.03851v2 fatcat:nz5wblhw5jd7nju64hewsik3sy

SoK: Cryptographically Protected Database Search [article]

Benjamin Fuller, and Mayank Varia, and Arkady Yerukhimovich, and Emily Shen, and Ariel Hamlin, and Vijay Gadepally, and Richard Shay, and John Darby Mitchell, Robert K. Cunningham
2017 arXiv   pre-print
these base operations.  ...  This evaluation describes the main approaches and tradeoffs for each base operation.  ...  Chase and Shen [109] design a protection method based on suffix trees to enable substring search. Tree-based indices are also amenable to range searches.  ... 
arXiv:1703.02014v2 fatcat:hev7mo27lnfspggsc4jtq4zrmy

SoK: Exploring Blockchains Interoperability [article]

Gang Wang
2021 IACR Cryptology ePrint Archive  
Also, the privacy-preserving technologies in current blockchain systems are not robust enough.  ...  Based on what we observed and learned, we discussed opportunities and challenges when applying blockchain interoperability into current blockchain design.  ... 
dblp:journals/iacr/Wang21a fatcat:qjtwv6yozbcblgsnev7al3z2oe

SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges [article]

Prajwal Panzade, Daniel Takabi
2022 arXiv   pre-print
Numerous efforts have been made in privacy-preserving machine learning (PPML) to address security and privacy concerns.  ...  We focus on Inner-product-FE and Quadratic-FE-based machine learning models for the PPML applications.  ...  This SoK paper aims to study several research works in the field of functional encryption-based privacy-preserving machine learning.  ... 
arXiv:2204.05136v1 fatcat:sah3enwbqzfe5pjqbnyjbjpdx4

SoK: Applying Blockchain Technology in Industrial Internet of Things [article]

Gang Wang
2021 IACR Cryptology ePrint Archive  
However, these adopted privacy-preserving technologies in current blockchain systems are not robust enough.  ...  Preserving data privacy is challenging due to its complexity, decentralization, and heterogeneity of IoT systems.  ... 
dblp:journals/iacr/Wang21b fatcat:ds2ekk2345hczowriucjedo6gu

SoK: Diving into DAG-based Blockchain Systems [article]

Qin Wang, Jiangshan Yu, Shiping Chen, Yang Xiang
2020 arXiv   pre-print
To bridge the gap, this Systematization of Knowledge (SoK) provides a comprehensive analysis of DAG-based blockchain systems.  ...  We further identify open challenges to highlight the potentiality of DAG-based solutions and indicate their promising directions for future research.  ...  No (effective) privacy-preserving solutions have been applied to the DAG-based systems up to now so far. Informal Model.  ... 
arXiv:2012.06128v2 fatcat:ntxxixxeevhhhj6tng5umornve

SoK: Understanding BFT Consensus in the Age of Blockchains [article]

Gang Wang
2021 IACR Cryptology ePrint Archive  
There are many excellent Byzantine-based replicated solutions and ideas that have been contributed to improving performance, availability, or resource efficiency.  ...  Based on the proposed APDB and asynchronous binary agreement, the authors design and present a self-bootstrap framework Dumbo-MVBA to reduce the communication cost of existing MVBA protocols.  ...  However, these adopted privacy-preserving technologies in current blockchain systems are not robust enough.  ... 
dblp:journals/iacr/Wang21c fatcat:wggbkbi25fg43fieebp4qzlk3m

SoK: The Evolution of Sybil Defense via Social Networks

L. Alvisi, A. Clement, A. Epasto, S. Lattanzi, A. Panconesi
2013 2013 IEEE Symposium on Security and Privacy  
We speculate that in the near future new defense layers based on advanced machine-learning and profiling techniques [32] will force a sybil attacker who wants to escape detection to generate sybil regions  ...  A second attack model: In this section we compare the algorithms using an attack model widely used in the literature [10] , [41] .  ... 
doi:10.1109/sp.2013.33 dblp:conf/sp/AlvisiCELP13 fatcat:42qjx64obfhoreqfikknnqcbne

On the Security Privacy in Federated Learning [article]

Gorka Abad, Stjepan Picek, Víctor Julio Ramírez-Durán, Aitor Urbieta
2022 arXiv   pre-print
Federated Learning (FL) grants a privacy-driven, decentralized training scheme that improves ML models' security.  ...  Recent privacy awareness initiatives such as the EU General Data Protection Regulation subdued Machine Learning (ML) to privacy and security assessments.  ...  Differential Privacy Differential Privacy is a privacy-preserving mechanism that adds noise to the model for limiting a wide range of attacks [1, 30, 91] .  ... 
arXiv:2112.05423v2 fatcat:qcovp2cz2rfgbcvx6mtx5xighe

SoK: An Analysis of Protocol Design: Avoiding Traps for Implementation and Deployment [article]

Tobias Fiebig, Franziska Lichtblau, Florian Streibelt, Thorben Krueger, Pieter Lexis, Randy Bush, Anja Feldmann
2016 arXiv   pre-print
IPFIX supports flexible definitions of network flows via a template based extensible information model.  ...  Thus, versatile security solutions to model complex organizational structures, e.g., via role-based access control (RBAC), were needed.  ... 
arXiv:1610.05531v1 fatcat:vaybjuis7rcnrnhlaetdhql6au
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