This presentation on Lattice-based Digital Signatures from April 2018 was given to the Chinese academy of science from OnBoard Security's Zhenfei Zhang.
論文紹介:End-to-End Object Detection with TransformersToru Tamaki
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov & Sergey Zagoruyko, "End-to-End Object Detection with Transformers" ECCV2020
https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/832_ECCV_2020_paper.php
Basics of RNNs and its applications with following papers:
- Generating Sequences With Recurrent Neural Networks, 2013
- Show and Tell: A Neural Image Caption Generator, 2014
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015
- Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016
- Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016
- Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016
- DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017
- Predictive State Recurrent Neural Networks, 2017
#10 pydata warsaw object detection with dn nsAndrew Brozek
PyData Warsaw #10: Deep & Machine Learning
Object detection with Deep Learning
These are the references for the first part of the talk.
1) a Stanford lecture
http://vision.stanford.edu/teaching/cs231n/slides/2016/winter1516_lecture8.pdf
2) OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
https://arxiv.org/abs/1312.6229
3) Selective Search for Object Recognition https://www.koen.me/research/selectivesearch/
4) Rich feature hierarchies for accurate object detection and semantic segmentation
https://arxiv.org/abs/1311.2524
5) Fast R-CNN
https://arxiv.org/abs/1504.08083
6) Faster R-CNN: Towards Real-Time Object
Detection with Region Proposal Networks
https://arxiv.org/pdf/1506.01497.pdf
7) A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
https://arxiv.org/abs/1704.03414
Personalizing "The Netflix Experience" with Deep LearningAnoop Deoras
These are the slides from my talk presented at AI Next Con conference in Seattle in Jan 2019. Here I talk in a bit more detail about the intuition behind collaborative filtering and go a bit deeper into the details of non linear deep learned models.
https://mcv-m6-video.github.io/deepvideo-2019/
This lecture provides an overview how the temporal information encoded in video sequences can be exploited to learn visual features from a self-supervised perspective. Self-supervised learning is a type of unsupervised learning in which data itself provides the necessary supervision to estimate the parameters of a machine learning algorithm.
Master in Computer Vision Barcelona 2019.
http://pagines.uab.cat/mcv/
論文紹介:End-to-End Object Detection with TransformersToru Tamaki
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov & Sergey Zagoruyko, "End-to-End Object Detection with Transformers" ECCV2020
https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/832_ECCV_2020_paper.php
Basics of RNNs and its applications with following papers:
- Generating Sequences With Recurrent Neural Networks, 2013
- Show and Tell: A Neural Image Caption Generator, 2014
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015
- Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016
- Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016
- Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016
- DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017
- Predictive State Recurrent Neural Networks, 2017
#10 pydata warsaw object detection with dn nsAndrew Brozek
PyData Warsaw #10: Deep & Machine Learning
Object detection with Deep Learning
These are the references for the first part of the talk.
1) a Stanford lecture
http://vision.stanford.edu/teaching/cs231n/slides/2016/winter1516_lecture8.pdf
2) OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
https://arxiv.org/abs/1312.6229
3) Selective Search for Object Recognition https://www.koen.me/research/selectivesearch/
4) Rich feature hierarchies for accurate object detection and semantic segmentation
https://arxiv.org/abs/1311.2524
5) Fast R-CNN
https://arxiv.org/abs/1504.08083
6) Faster R-CNN: Towards Real-Time Object
Detection with Region Proposal Networks
https://arxiv.org/pdf/1506.01497.pdf
7) A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
https://arxiv.org/abs/1704.03414
Personalizing "The Netflix Experience" with Deep LearningAnoop Deoras
These are the slides from my talk presented at AI Next Con conference in Seattle in Jan 2019. Here I talk in a bit more detail about the intuition behind collaborative filtering and go a bit deeper into the details of non linear deep learned models.
https://mcv-m6-video.github.io/deepvideo-2019/
This lecture provides an overview how the temporal information encoded in video sequences can be exploited to learn visual features from a self-supervised perspective. Self-supervised learning is a type of unsupervised learning in which data itself provides the necessary supervision to estimate the parameters of a machine learning algorithm.
Master in Computer Vision Barcelona 2019.
http://pagines.uab.cat/mcv/
Tutorial on People Recommendations in Social Networks - ACM RecSys 2013,Hong...Anmol Bhasin
Tutorials at ACM RecSys 2013
Social Networks
Learning to Rank
Beyond Friendship
Pref. Handling
Beyond Friendship: The Art, Science and Applications of Recommending People to People in Social Networks
by Luiz Augusto Pizzato (University of Sydney, Australia)
& Anmol Bhasin (LinkedIn, USA)
While Recommender Systems are powerful drivers of engagement and transactional utility in social networks, People recommenders are a fairly involved and diverse subdomain. Consider that movies are recommended to be watched, news is recommended to be read, people however, are recommended for a plethora of reasons – such as recommendation of people to befriend, follow, partner, targets for an advertisement or service, recruiting, partnering romantically and to join thematic interest groups.
This tutorial aims to first describe the problem domain, touch upon classical approaches like link analysis and collaborative filtering and then take a rapid deep dive into the unique aspects of this problem space like Reciprocity, Intent understanding of recommender and the recomendee, Contextual people recommendations in communication flows and Social Referrals – a paradigm for delivery of recommendations using the Social Graph. These aspects will be discussed in the context of published original work developed by the authors and their collaborators and in many cases deployed in massive-scale real world applications on professional networks such as LinkedIn.
Introduction
The basics of Social Recommenders
People recommender systems
Special Topics in People Recommenders
Why reciprocal (people) recommenders are different to traditional (product) recommendations
Multi-Objective Optimization
Intent Understanding
Feature Engineering
Social Referral
Pathfinding
Concluding remarks
The pre-requisite for this tutorial is some familiarity with foundational Recommender Systems, Data Mining, Machine Learning and Social Network Analysis literature.
Date
Oct 13, 2013 (08:30 – 10:15)
At Netflix we take context of the member seriously.
In this keynote talk we will see how modeling contextual factors such as time or device can help members to find the right content at the right moment
At the end, the goal is to maximize member satisfaction and retention
These slides will go through which contextual factors matters for the video service and why we choose to use them or not.
Polygon ID offers tools that allow developers to build self-sovereign, decentralized and private identity solutions for users that leverage zero knowledge proofs. Polygon ID was released as open source last March 2023 at ETH Denver. In this presentation, Otto Mora, BD Lead for Americas, and Oleksander Brezhniev, Technical Lead at Polygon ID, will be covering aspects of the did:PolygonID method including: Verifiable presentations leveraging ZK Proofs; How the Proofs are generated; Credential Issuance Methods; and Identity Management Features.
Blockchain overview, use cases, implementations and challengesSébastien Tandel
Most know about Bitcoin, the well-known crypto-currency. Less know the details about the underlying and enabling technology, Blockchain.
Hopefully, this presentation provides enough insights to understand blockchain concepts and why it's perceived to potentially disrupt many market segments, from retail to governments, from finance to health care. At last, I hope to brush fairly the many challenges of this rather new technology.
https://imatge.upc.edu/web/publications/keyframe-based-video-summarization-designer
This Final Degree Work extends two previous projects and consists in carrying out an improvement of the video keyframe extraction module from one of them called Designer Master, by integrating the algorithms that were developed in the other, Object Maps.
Firstly the proposed solution is explained, which consists in a shot detection method, where the input video is sampled uniformly and afterwards, cumulative pixel-to-pixel difference is applied and a classifier decides which frames are keyframes or not.
Last, to validate our approach we conducted a user study in which both applications were compared. Users were asked to complete a survey regarding to different summaries created by means of the original application and with the one developed in this project. The results obtained were analyzed and they showed that the improvement done in the keyframes extraction module improves slightly the application performance and the quality of the generated summaries.
OpenID for Verifiable Credentials is a family of protocols supporting implementation of applications with Verifiable Credentials, i.e. verifiable credential issuance, credential presentation, and pseudonyms authentication.
Self-supervised learning (SSL) is behind some of the latest AI breakthroughs. By enabling learning from vast amounts of unlabeled data, rather than relying on carefully annotated datasets, it has unlocked the potential of AI across Natural language processing, audio and computer vision. The talk covers how it is being used for vision tasks.
Presented in AI & ML meetup @Azuga on July 01, 2023 by Vidhya Vinay.
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
Abstract:
Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
Learning to discover monte carlo algorithm on spin ice manifoldKai-Wen Zhao
The global update Monte Carlo sampler can be discovered naturally by trained machine using policy gradient method on topologically constrained environment.
SECRYPT 2018 Presentation: 15th International Conference on Security and Cry...Mriganka Mandal
Title of the presentation: Cost-Effective Private Linear Key Agreement With Adaptive CCA Security from Prime Order Multilinear Maps and Tracing Traitors
Authors: Mriganka Mandal and Ratna Dutta
Accepted: SECRYPT 2018
Date and Venue: July 26 - 28, 2018 Porto, Portugal
Tutorial on People Recommendations in Social Networks - ACM RecSys 2013,Hong...Anmol Bhasin
Tutorials at ACM RecSys 2013
Social Networks
Learning to Rank
Beyond Friendship
Pref. Handling
Beyond Friendship: The Art, Science and Applications of Recommending People to People in Social Networks
by Luiz Augusto Pizzato (University of Sydney, Australia)
& Anmol Bhasin (LinkedIn, USA)
While Recommender Systems are powerful drivers of engagement and transactional utility in social networks, People recommenders are a fairly involved and diverse subdomain. Consider that movies are recommended to be watched, news is recommended to be read, people however, are recommended for a plethora of reasons – such as recommendation of people to befriend, follow, partner, targets for an advertisement or service, recruiting, partnering romantically and to join thematic interest groups.
This tutorial aims to first describe the problem domain, touch upon classical approaches like link analysis and collaborative filtering and then take a rapid deep dive into the unique aspects of this problem space like Reciprocity, Intent understanding of recommender and the recomendee, Contextual people recommendations in communication flows and Social Referrals – a paradigm for delivery of recommendations using the Social Graph. These aspects will be discussed in the context of published original work developed by the authors and their collaborators and in many cases deployed in massive-scale real world applications on professional networks such as LinkedIn.
Introduction
The basics of Social Recommenders
People recommender systems
Special Topics in People Recommenders
Why reciprocal (people) recommenders are different to traditional (product) recommendations
Multi-Objective Optimization
Intent Understanding
Feature Engineering
Social Referral
Pathfinding
Concluding remarks
The pre-requisite for this tutorial is some familiarity with foundational Recommender Systems, Data Mining, Machine Learning and Social Network Analysis literature.
Date
Oct 13, 2013 (08:30 – 10:15)
At Netflix we take context of the member seriously.
In this keynote talk we will see how modeling contextual factors such as time or device can help members to find the right content at the right moment
At the end, the goal is to maximize member satisfaction and retention
These slides will go through which contextual factors matters for the video service and why we choose to use them or not.
Polygon ID offers tools that allow developers to build self-sovereign, decentralized and private identity solutions for users that leverage zero knowledge proofs. Polygon ID was released as open source last March 2023 at ETH Denver. In this presentation, Otto Mora, BD Lead for Americas, and Oleksander Brezhniev, Technical Lead at Polygon ID, will be covering aspects of the did:PolygonID method including: Verifiable presentations leveraging ZK Proofs; How the Proofs are generated; Credential Issuance Methods; and Identity Management Features.
Blockchain overview, use cases, implementations and challengesSébastien Tandel
Most know about Bitcoin, the well-known crypto-currency. Less know the details about the underlying and enabling technology, Blockchain.
Hopefully, this presentation provides enough insights to understand blockchain concepts and why it's perceived to potentially disrupt many market segments, from retail to governments, from finance to health care. At last, I hope to brush fairly the many challenges of this rather new technology.
https://imatge.upc.edu/web/publications/keyframe-based-video-summarization-designer
This Final Degree Work extends two previous projects and consists in carrying out an improvement of the video keyframe extraction module from one of them called Designer Master, by integrating the algorithms that were developed in the other, Object Maps.
Firstly the proposed solution is explained, which consists in a shot detection method, where the input video is sampled uniformly and afterwards, cumulative pixel-to-pixel difference is applied and a classifier decides which frames are keyframes or not.
Last, to validate our approach we conducted a user study in which both applications were compared. Users were asked to complete a survey regarding to different summaries created by means of the original application and with the one developed in this project. The results obtained were analyzed and they showed that the improvement done in the keyframes extraction module improves slightly the application performance and the quality of the generated summaries.
OpenID for Verifiable Credentials is a family of protocols supporting implementation of applications with Verifiable Credentials, i.e. verifiable credential issuance, credential presentation, and pseudonyms authentication.
Self-supervised learning (SSL) is behind some of the latest AI breakthroughs. By enabling learning from vast amounts of unlabeled data, rather than relying on carefully annotated datasets, it has unlocked the potential of AI across Natural language processing, audio and computer vision. The talk covers how it is being used for vision tasks.
Presented in AI & ML meetup @Azuga on July 01, 2023 by Vidhya Vinay.
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
Abstract:
Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
Learning to discover monte carlo algorithm on spin ice manifoldKai-Wen Zhao
The global update Monte Carlo sampler can be discovered naturally by trained machine using policy gradient method on topologically constrained environment.
SECRYPT 2018 Presentation: 15th International Conference on Security and Cry...Mriganka Mandal
Title of the presentation: Cost-Effective Private Linear Key Agreement With Adaptive CCA Security from Prime Order Multilinear Maps and Tracing Traitors
Authors: Mriganka Mandal and Ratna Dutta
Accepted: SECRYPT 2018
Date and Venue: July 26 - 28, 2018 Porto, Portugal
Apresentação sobre Criptografia baseada em reticulados (lattices), realizada no contexto da disciplina de Post-Quantum Cryptography do PPGCC da UFSC.
Versão odp: http://coenc.td.utfpr.edu.br/~giron/presentations/aula_lattice.odp
THE KEY EXCHANGE CRYPTOSYSTEM USED WITH HIGHER ORDER DIOPHANTINE EQUATIONSIJNSA Journal
One-way functions are widely used for encrypting the secret in public key cryptography, although they are regarded as plausibly one-way but have not been proven so. Here we discuss the public key cryptosystem based on the system of higher order Diophantine equations. In this system those Diophantine equations are used as public keys for sender and recipient, and both sender and recipient can obtain the shared secret through a trapdoor, while attackers must solve those Diophantine equations without trapdoor. Thus the scheme of this cryptosystem might be considered to represent a possible one-way function. We also discuss the problem on implementation, which is caused from additional complexity necessary for constructing Diophantine equations in order to prevent from attacking by tamperers.
LITTLE DRAGON TWO: AN EFFICIENT MULTIVARIATE PUBLIC KEY CRYPTOSYSTEMIJNSA Journal
In 1998 [8], Patarin proposed an efficient cryptosystem called Little Dragon which was a variant a variant of Matsumoto Imai cryptosystem C*. However Patarin latter found that Little Dragon cryptosystem is not secure [8], [3]. In this paper we propose a cryptosystem Little Dragon Two which is as efficient as Little Dragon cryptosystem but secure against all the known attacks. Like Little Dragon cryptosystem the public key of Little Dragon Two is mixed type that is quadratic in plaintext and cipher text variables. So the public key size of Little Dragon Two is equal to Little Dragon Cryptosystem. Our public key algorithm is bijective and can be used for both encryption and signatures.
Robust Cascade Reconstruction by Steiner Tree SamplingCigdem Aslay
We consider a network where an infection has taken place
and a subset of infected nodes has been partially observed.
Our goal is to reconstruct the underlying cascade that is likely to have generated these observations. We reduce this cascade-reconstruction problem to computing the marginal probability
that a node is infected given the partial observations, which is a #P-hard problem. To circumvent this issue, we resort to estimating infection probabilities by generating a sample of probable cascades, which span the nodes that have already been observed to be infected, and avoid the nodes that have been observed to be uninfected. The sampling problem corresponds to sampling directed Steiner trees with a given set of terminals, which is a problem of independent interest and has received limited attention in the literature. For the latter problem we propose two novel algorithms with provable guarantees on the sampling distribution of the returned Steiner trees.
The resulting method improves over state-of-the-art approaches that often make explicit assumptions about the infection-propagation model, or require additional parameters. Our method provides a more robust approach to the cascade-reconstruction problem, which makes weaker assumptions about the infection model, requires fewer additional parameters, and can be used to estimate node infection probabilities. We experimentally validate the proposed reconstruction algorithm on real-world graphs with both synthetic and real cascades. We show that our method outperforms all other baseline strategies in most cases.
To describe the dynamics taking place in networks that structurally change over time, we propose an approach to search for attributes whose value changes impact the topology of the graph. In several applications, it appears that the variations of a group of attributes are often followed by some structural changes in the graph that one may assume they generate. We formalize the triggering pattern discovery problem as a method jointly rooted in sequence mining and graph analysis. We apply our approach on three real-world dynamic graphs of different natures - a co-authoring network, an airline network, and a social bookmarking system - assessing the relevancy of the triggering pattern mining approach.
Seminar of Threshold-optimal DSAECDSA signatures and an application to Bitcoin wallet security - ACNS 2016 by Rosario Gennaro, Steven Goldfeder, and Arvind Narayanan
Turning Krimp into a Triclustering Technique on Sets of Attribute-Condition P...Dmitrii Ignatov
Mining ternary relations or triadic Boolean tensors is one of the recent trends in knowledge discovery that allows one to take into account various modalities of input object-attribute data.
For example, in movie databases like IMBD, an analyst may find not only movies grouped by specific genres but see their common keywords. In the so called folksonomies, users can be grouped according to their shared resources and used tags. In gene expression analysis, genes can be grouped along with samples of tissues and time intervals providing comprehensible patterns. However, pattern explosion effects even with one more dimension are seriously aggravated. In this paper, we continue our previous study on searching for a smaller collection of ``optimal'' patterns in triadic data with respect to a set of quality criteria such as patterns' cardinality, density, diversity, coverage, etc. We show how a simple data preprocessing has enabled us to use the frequent itemset mining algorithm.
In this paper we study of the MOR cryptosystem using camina group. We show that using the automorphism of the camina group one can build a secure MOR cryptosystem.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This presentation discusses the use of Garbled Circuits for improving security and simplifying implementation of Secure Credential Management Systems (SCMS) in the Automotive industry
The presentation "Secure Drone-to-X Communication: Applicability of IEEE 1609.2" from OnBoard Security's Drew Van Duren and Jonathan Petit explores using Aerolink V2X security libraries with Unmanned Aerial Vehicles.
This presentation by OnBoard Security's Drew van Duren was given at the IEEE 4th World Forum on Internet of Things
05-08 February 2018 in Singapore. Topics covered include:
– Connected Vehicle Architectures and Applications
– IEEE 1609.2 V2X security stack and uses
– Issues and Lessons Learned in U.S. CV Pilots
– Potential Unmanned aircraft systems (Drones) applications
– Re-tasking V2X security to other uses
Presentation given at WiSec 2017 by Dr. Virendra Kumar. His, along with Drs. Jonathan Petit and William Whyte's, paper was one of six to receive the reproducibility label.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
PHP Frameworks: I want to break free (IPC Berlin 2024)
Lattice-based Signatures
1. Lattice based signatures
Zhenfei Zhang
zzhang@onboardsecurity.com
April 27, 2018
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 1 / 29
2. Our company
Previously known as NTRU Cryptosystem Inc., . . .
. . . then Security Innovation, . . .
Three focus area:
Lattice based cryptographic research;
V2X security;
Editor of IEEE 1609.2 WAVE standard
Trusted Computing and TPMs;
Chair for TCG software stack working group and Virtualized Platform
working group
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 2 / 29
5. Why lattice
Lattice leads to the knowledge of everything!
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 3 / 29
6. Why lattice
Lattice leads to the knowledge of everything!
(WRONG!)
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 3 / 29
7. Why lattice
the real reason
1994, Shor’s algorithm, break RSA and ECC with quantum
computers;
2015, NSA announcement: prepare for the quantum apocalypse;
2017, NIST call for competition/standardization;
2030(?), predicted general purpose quantum computers;
bonus points
Good understanding of underlying hard problem;
Fast, parallelable, hardware friendly;
Numerous applications: FHE, ABE, MMap, obfuscation, . . .
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 4 / 29
8. Why lattice
the real reason
2030(?), predicted general purpose quantum computers;
Data vaulting attack
A.k.a., harvest-then-decrypt attack
Data need to be secret for, say, 30 years;
Quantum computer arrives in, say, 15 years;
Perhaps the most practical attack in cryptography!
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 5 / 29
14. Figure source: Wendy Cordero’s High School Math Site
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 11 / 29
15. Lattice
Definition of a Lattice
All the integral combinations of d ≤ n linearly independent vectors
over R
L = Z b1 + · · · + Z bd = {λ1b1 + · · · + λd bd : λi ∈ Z}
d dimension.
B = (b1, . . . , bd ) is a basis.
An example
B =
5 1
2
√
3
3
5
√
2 1
d = 2 ≤ n = 3
In this talk, full rank integer Basis: B ∈ Zn,n.
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 12 / 29
16. Example
A lattice L
B =
8 5
5 16
All lattice crypto talks start with an image of a dim-2 lattice
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 13 / 29
17. Example
A lattice L
UB =
1 0
−1 1
8 5
5 16
=
8 5
−3 11
An infinity of basis
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 13 / 29
18. Example
A lattice L
UB =
1 0
1 1
8 5
5 16
=
8 5
13 21
An infinity of basis
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 13 / 29
19. Example
A lattice L
UB =
3 1
2 1
8 5
5 16
=
29 31
21 26
An infinity of basis
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20. Example
The Shortest Vector and The First Minima
v = 8 5 , with λ1 = 82 + 52 = 9.434
The Shortest Vector
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 13 / 29
21. Example
The Determinant
det L = det (BBT ) = 103
The Fundamental Parallelepiped
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 13 / 29
22. NTRU lattice
NTRU ring
Originally: Zq[x]/(xN − 1), q a power of 2, N a prime;
Alternative 1: Zq[x]/(xN − x − 1), q a prime;
Alternative 2: Zq[x]/(xN + 1), q a prime, N a power of 2
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 14 / 29
23. NTRU lattice
NTRU ring
Originally: Zq[x]/(xN − 1), q a power of 2, N a prime;
Alternative 1: Zq[x]/(xN − x − 1), q a prime;
Alternative 2: Zq[x]/(xN + 1), q a prime, N a power of 2
Ring multiplications: h(x) = f (x) · g(x)
Compute h (x) = f (x) × g(x) over Z[x]
Reduce h (x) mod (xN − 1) mod q
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 14 / 29
24. NTRU lattice
NTRU ring
Originally: Zq[x]/(xN − 1), q a power of 2, N a prime;
Alternative 1: Zq[x]/(xN − x − 1), q a prime;
Alternative 2: Zq[x]/(xN + 1), q a prime, N a power of 2
Ring multiplications: h(x) = f (x) · g(x), alternatively
h0, . . . , hN−1 = f0, . . . , fN−1 ×
g0 g1 g2 . . . gN−1
gN−1 g0 g1 . . . gN−2
gN−2 gN−1 g0 . . . gN−3
...
...
...
...
...
g1 g2 g3 . . . g0
mod q
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 14 / 29
25. NTRU lattice
NTRU assumption
Decisional: given two small ring elements f and g; it is hard to
distinguish h = f /g from a uniformly random ring element;
Computational: given h, find f and g.
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 15 / 29
26. NTRU lattice
NTRU assumption
Decisional: given two small ring elements f and g; it is hard to
distinguish h = f /g from a uniformly random ring element;
Computational: given h, find f and g.
NTRU lattice
qIN 0
H IN
..=
q 0 . . . 0 0 0 . . . 0
0 q . . . 0 0 0 . . . 0
...
...
...
...
...
...
...
...
0 0 . . . q 0 0 . . . 0
h0 h1 . . . hN−1 1 0 . . . 0
hN−1 h0 . . . hN−2 0 1 . . . 0
...
...
...
...
...
...
...
...
h1 h2 . . . h0 0 0 . . . 1
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 15 / 29
27. NTRU lattice
NTRU assumption
Decisional: given two small ring elements f and g; it is hard to
distinguish h = f /g from a uniformly random ring element;
Computational: given h, find f and g.
NTRU lattice L =
qIN 0
H IN
g, f (and its cyclic rotations) are unique shortest vectors in L;
Decisional problem: decide if L has unique shortest vectors;
Computational problem: find those vectors.
Both are hard for random lattices.
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 15 / 29
28. NTRU lattice
The real NTRU assumption
NTRU lattice behaves the same as random lattices.
NTRU lattice L =
qIN 0
H IN
g, f (and its cyclic rotations) are unique shortest vectors in L;
Decisional problem: decide if L has unique shortest vectors;
Computational problem: find those vectors.
Both are hard for random lattices.
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 15 / 29
29. NTRU lattice vs random lattice
256 0
172 1
256 0
17 1
(g, f ) = (1, 3) v = (17, 1)
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31. GGHSign
Signing key: a good basis B
Verification key a bad basis H
Sign
Hash message to a vector v
Use B to find the closest vector c (Babai’s algorithm)
Verification
Check Dist(v − c) is small
NTRUSign
Good basis: (g,f)
Bad basis: h
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32. Transcript security
Breaks GGHSign, NTRUSign;
Each signature is a vector close
to the lattice (info leakage);
Recover enough of distance
vectors (blue dots) gives away a
good basis of the lattice;
Seal the leakage with rejection
sampling.
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 19 / 29
33. GPV sampler: a randomized Babai function
The idea
A trapdoored lattice L, i.e.
L⊥
A := {v : Av = 0 mod q}, Lh := {(u, v) : uh = v mod q}
A trapdoor S, or (g, f ), and a smooth parameter ηε(L)
A target lattice point v
Outputs another vector s, s.t.
s is uniform over L
dist(s, v) Gaussian over Zn
Bottle neck: trapdoor generation
Bonsai Tree, Gadget matrix, . . .
Falcon = GPV + NTRUSign + more ticks
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 20 / 29
35. Falcon
Public key security: recover f and g from h;
Forgery: as hard as finding a preimage for GPV without secret key
Transcript security: output is already Gaussian
independent from secret basis; no need for rejection sampling.
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 22 / 29
36. Modular Lattice Signatures
The core idea
Given a lattice L with a trapdoor T, a message m, find a vector v
v ∈ L
v ≡ hash(m) mod p
Can be instantiated via any trapdoored lattice
SIS, R-SIS, R-LWE, etc
pqNTRUSign is an efficient instantiation using NTRU lattice
Efficient trapdoor f , g.
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 23 / 29
37. pqNTRUSign
Sign (f , g, h = g/f , p = 3, R, m)
Hash message into a “mod p” vector vp, up = hash(m|h)
Repeat with rejection sampling:
Sample v0 from certain distribution; compute v1 = p × v0 + vp
Find a random lattice vector v1, u1 = v1 · I, h
“v-side” meets the congruent condition.
Micro-adjust “u-side” using trapdoor f and g
Compute a = (u1 − up) · g−1
mod p
Compute v2, u2 = a · p × f , g
Compute v, u = v1, u1 + v2, u2
Output v as signature
Remark
v = v1 + v2 = (p × v0 + vp) + p × a · f = p × (v0 + a · f ) + vp
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 24 / 29
38. pqNTRUSign
Verify (h, p = 3, R, m, v)
Hash message into a “mod p” vector vp, up = hash(m|h)
Reconstruct the lattice vector v, u = v · I, h
Check vp, up = hash(m|h)
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 25 / 29
39. pqNTRUSign
Public key security: recover f and g from h;
Forgery: as hard as solving an approx.-SVP in an intersected lattice;
Transcript security - achieved via rejection sampling.
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 26 / 29
40. Rejection Sampling
Consider b ..= v0 + a · f
“large” v0 drawn from uniform or Gaussian;
“small” a drawn from sparse trinary/binary;
sparse trinary/binary f is the secret.
RS on b
b follows certain publicly known distribution independent from f ;
for two secret keys f1, f2 and a signature b, one is not able to tell
which key signs b - witness indistinguishability.
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 27 / 29
41. Rejection Sampling
Rejection sampling on Uniform
Sample v0 uniformly from [−q
2 , q
2 ]N
Accept b when b is in [−q
2 + B, q
2 − B]N
Before rejection
0.0005
0.0006
0.0007
0.0008
0.0009
0.001
0.0011
-600 -400 -200 0 200 400 600
"notuniforminq"
1/1031.0
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 28 / 29
42. Rejection Sampling
Rejection sampling on Uniform
Sample v0 uniformly from [−q
2 , q
2 ]N
Accept b when b is in [−q
2 + B, q
2 − B]N
After rejection
0
0.0002
0.0004
0.0006
0.0008
0.001
0.0012
-600 -400 -200 0 200 400 600
"uniforminq"
1/1021.0
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 28 / 29
43. Rejection Sampling
Rejection sampling on Gaussian
Sample v0 from discrete Gaussian χN
σ
Accept b when b is Gaussian
Before/after rejection
Z.Zhang (OnBoard Security Inc.) NTRU crypto April 27, 2018 28 / 29
44. Thanks!
to study the underlying principle to acquire knowledge (idiom);
pursuing knowledge to the end.
Figure source: Google Image & www.hsjushi.com
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