1,720,959 research outputs found
Blockchain driven secure and private machine learning algorithms for post-quantum 5G/6G enabled industrial IOT with applications to cyber-security and health
Get PDFWe provide a general framework for secure and private multi-label multi-output machine learning (ML) algorithms for the semi-honest model in distributed edge IoT (Internet of Things) environments enabled by 5G/6G networks. The proposed framework includes the special cases of binary, multi-class and multi-label ML algorithms. We deal with both horizontally and vertically partitioned datasets. Initially, (i) we propose novel secure feature selection protocols by homomorphically evaluating features’ information gains in distributed environments, we proceed with (ii) novel secure training protocols over the set of selected features, then (iii) we propose novel secure building blocks which are commonly used on ML algorithms (e.g. secure sum, comparison, argmax, top-K, sorting, permutation, etc.), as well as on secure linear algebra (e.g. secure inner product, cascading matrix-vector and matrix-matrix multiplications, matrix transpose, etc.), and finally (iv) on top of proposed secure building blocks we build our novel secure ML classification protocols for various ML classifiers such as Deep Neural Networks (DNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests (RF), different flavors of Naïve Bayes (NB), Logistic Regression (LR) and K Nearest Neighbors (KNN). Moreover, our secure classification protocols also deal with malicious users that arbitrarily deviate from the protocol and they show no loss of accuracy due to secure classifications. In the process, our participants interact with each other in order to fulfill strict security. privacy and efficiency requirements. To these ends, we provide confidentiality, integrity and authenticity to each interaction by signing their hashed contents with the corresponding participants’ private key. We assure the consistency among interactions by introducing timestamps and linking them with the hashed content(s) of the preceding interaction(s). This makes our protocols a natural fit for blockchain technology. Moreover, the proposed cryptographic tools are proven to be resistant to quantum computer attacks, making our protocols applicable to the post quantum world. We did our theoretical analysis and extensive experimental evaluations over benchmark datasets related to cyber-security and health. They show that our protocols have an advantage ranging from several times to orders of magnitudes with respect to the state-of-the-art in terms of computation and communication costs. This makes our protocols among the most efficient ones in literature. Also, they are among the best in terms of security and privacy properties and allow high rate of fault tolerance and collusion attacks of dataset owners with respect to the state-of-the-art
A Constant Time Secure and Private Evaluation of Decision Trees in Smart Cities Enabled by Mobile IoT
No full textA server has an already trained decision tree machine learning model and one or more clients have unclassified query(ies) that they wish to classify using the server's model under strict security, privacy, and efficiency constraints. To do so, already existing secure building blocks are used, improved, and adjusted to fit this scenario. On top of the proposed building blocks, novel secure and private Decision Tree Evaluation (sDTE) algorithms are proposed. The proposed building blocks show better performances than the related ones in literature in terms of computation and communication costs. Consequently, experimental evaluations over benchmark datasets show that the proposed sDTE algorithms build on top of the proposed blocks, also outperform the state-of-the-art ones in terms of computation and communication costs as well as on security and privacy characteristics. Our theoretical analysis shows that if the whole decision tree can fit in a single ciphertext, which in the proposed sDTE algorithms is almost always the case, then private tree evaluations are done in constant time and do not depend on the tree depth. To the best of the author's knowledge, this is the first scheme in literature with such properties
Covert, secure and private communications in software defined networking
No full textInternational Conference on Computational Science and Computational Intelligence (CSCI) -- DEC 13-15, 2023 -- Las Vegas, NVCovert and private communications are an essential part of modern Internet. While there have been several works on these topics, almost all of them have one or more drawbacks, which are unacceptable in modern requirements of Software Defined Networking (SDN). To this end, initially, we propose and adopt a few secure buildings blocks, such as secure bit stream match, secure longest prefix match, secure header replication, etc. On top of them we propose covert and secure end-to-end generalized forwarding protocols used in SDN over busy routers. Besides covert communications, communication privacy is a by-product of the proposed protocols. The proposed schemes are proven to be secure under the semi-honest model, outperform in the related schemes in terms of security and privacy characteristics, while the experimental evaluations show their computation and communication efficiency.IEE
Privacy-Preserving Zero-Sum-Path Evaluation of Decision Tress in Postquantum Industrial IoT
No full textA server has a trained machine learning model in the form of a decision tree (DT), while one or more client(s) have unlabeled queries that they wish to classify using the server's model under strict security, privacy, and efficiency requirements on both sides. To do so, initially, based on lightweight cryptographic primitives, which are shown to be resistant to quantum computer attacks, a few secure buildings are adopted, improved, and adjusted to fit this scenario. On top of them, a novel secure and private DT evaluation and its extension over malicious clients protocols are proposed, which are both proven to be secure. In the process, we use the sum of paths of inner nodes from the root to the leaves of the DT, which in turn utilizes the comparison of threshold values of the tree nodes and the corresponding query feature values (entries). Theoretical analysis and extensive experimental evaluations over benchmark datasets show that the proposed protocols outperform the majority (if not all) of the related state-of-the-art schemes in terms of computation and communication costs as well as on security and privacy characteristics. Furthermore, the proposed protocols are shown to be resistant to side-channel attacks. This makes the proposed protocol suitable for the postquantum world of the industrial Internet of Things, which demands strict security and privacy requirements on devices with restricted hardware/networking resources
Efficient secure building blocks with application to privacy preserving machine learning algorithms
No full textNowadays different entities (such as hospitals, cyber security companies, banks, etc.) collect data of the same nature but often with different statistical properties. It has been shown that if these entities combine their privately collected datasets to train a machine learning model, they would end up with a trained model that often outperforms the human experts of the corresponding field(s) in terms of classification accuracy. However, due to judicial, privacy and cost reasons, no entity is willing to share their data with others. We have the same problem during the classification (inference) stage. Namely, the user doesn't want to reveal any information about his query or its' final classification, while the owner of the trained model wants to keep this model private. In this article we overcome these drawbacks by firstly introducing novel efficient secure building blocks for general purpose, which can also be used to build privacy preserving machine learning algorithms for both training and classification (inference) purposes under strict privacy and security requirements. Our theoretical analysis and experimentation results show that our building blocks (hence also our privacy preserving algorithms which are built on top of them) are more efficient than most (if not all) of the state-of-the-art schemes in terms of computation and communication cost, as well as security characteristics in the semi-honest model. Furthermore, and to the best of our knowledge, for the Naïve Bayes model we extend this efficiency for the first time to also deal with active malicious users, which arbitrarily deviate from the protocol
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Highly Efficient Secure Linear Algebra for Private Machine Learning Classifications over Malicious Clients in the Post-Quantum World
No full textOver the last decade there has a been widespread usage of Machine Learning (ML) classifiers in cases such accurate disease diagnosis at clinics, credit card fraud detection in banks, cyber-attacks prevention of computer systems in different industries, etc. However, privacy and security concerns and law regulations have been an obstacle to the usage of ML classifiers. To this end, this paper addresses the scenario where a server has a private trained ML model, and one or more clients have private queries that they wish to classify using the server's model. During the process, the server learns nothing, while the clients learn only their final classifications and nothing else. Several ML classification algorithms, such as Deep Neural Networks, Support Vector Machines, Logistic Regression, different flavors of Naïve Bayes, etc., can be expressed in terms of linear algebra operations. To this end, initially, as building blocks, several novel secure linear algebra operations are proposed. On top of them novel secure ML classification algorithms are proposed for the aforementioned classifiers under strict security, privacy and efficiency constraints and their security is proven under the semi-honest model. Since the used underlying cryptographic primitives are shown to be resilient to quantum computer attacks, the proposed algorithms are also suitable for the post-quantum world. Furthermore, the proposed algorithms are non-interactive and, based on where the bulk of the operations are done, they have the flexibility to be server or client centric. Theoretical analysis and extensive experimental evaluations over benchmark datasets show that the proposed secure linear algebra operations, hence the secure ML algorithms build on top of them, outperform the state-of-the-art schemes in terms of computation and communication costs as well as on security and privacy characteristics. Moreover, and to the best of the authors’ knowledge, for the first time in literature the security of the proposed algorithms is proven when dealing with multiple malicious clients during classifications
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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