A Survey on the Evolution of Stream Processing Systems

Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. This survey provides a comprehensive overview of fundamental aspects of stream processing systems and their evolution in the functional areas of out-of-order data management, state management, fault tolerance, high availability, load management, elasticity, and reconfiguration. We review noteworthy past research findings, outline the similarities and differences between early ('00-'10) and modern ('11-'18) streaming systems, and discuss recent trends and open problems.Comment: 34 pages, 15 figures, 5 table

Digital Watermarking Security

As creative works (e.g. books, films, music, photographs) become increasingly available in digital formats in a highly connected world, it also becomes increasingly difficult to secure intellectual property rights. Digital watermarking is one potential technology to aid intellectual property owners in controlling and tracking the use of their works. Surveys the state of digital watermarking research and examines the attacks that the technology faces and how it fares against them. Digital watermarking is an inherently difficult design problem subject to many constraints. The technology currently faces an uphill battle to be secure against relatively simple attacks

Deep Intellectual Property: A Survey

With the widespread application in industrial manufacturing and commercial services, well-trained deep neural networks (DNNs) are becoming increasingly valuable and crucial assets due to the tremendous training cost and excellent generalization performance. These trained models can be utilized by users without much expert knowledge benefiting from the emerging ''Machine Learning as a Service'' (MLaaS) paradigm. However, this paradigm also exposes the expensive models to various potential threats like model stealing and abuse. As an urgent requirement to defend against these threats, Deep Intellectual Property (DeepIP), to protect private training data, painstakingly-tuned hyperparameters, or costly learned model weights, has been the consensus of both industry and academia. To this end, numerous approaches have been proposed to achieve this goal in recent years, especially to prevent or discover model stealing and unauthorized redistribution. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field. More than 190 research contributions are included in this survey, covering many aspects of Deep IP Protection: challenges/threats, invasive solutions (watermarking), non-invasive solutions (fingerprinting), evaluation metrics, and performance. We finish the survey by identifying promising directions for future research.Comment: 38 pages, 12 figure

A survey on security analysis of machine learning-oriented hardware and software intellectual property

Intellectual Property (IP) includes ideas, innovations, methodologies, works of authorship (viz., literary and artistic works), emblems, brands, images, etc. This property is intangible since it is pertinent to the human intellect. Therefore, IP entities are indisputably vulnerable to infringements and modifications without the owner’s consent. IP protection regulations have been deployed and are still in practice, including patents, copyrights, contracts, trademarks, trade secrets, etc., to address these challenges. Unfortunately, these protections are insufficient to keep IP entities from being changed or stolen without permission. As for this, some IPs require hardware IP protection mechanisms, and others require software IP protection techniques. To secure these IPs, researchers have explored the domain of Intellectual Property Protection (IPP) using different approaches. In this paper, we discuss the existing IP rights and concurrent breakthroughs in the field of IPP research; provide discussions on hardware IP and software IP attacks and defense techniques; summarize different applications of IP protection; and lastly, identify the challenges and future research prospects in hardware and software IP security

Review on Seuring Data by Using Data Leakage Prevention and Detection

Today?s life everything including digital economy, data enter and leaves cyberspace at record rates. A typical enterprise sends and receives millions of email messages and downloads, saves, and transfers thousands of files via various channels on a daily basis. Enterprises also hold sensitive data that customers, business partners, regulators, and shareholders expect them to protect. While doing business we need to maintain the sensitive and confidential data. If the confidential data is leaked from the organization then it may influence on the organization heath. So preventing the data many vendors currently offer data leak prevention and detection products; surprisingly, however, there is one technique which is data leak prevention and detection, in this paper review on that Data Leak Prevention and Detection method. Here first term is data leak. Data leaks involve the release of sensitive information to an third party which is unauthorized user intentionally. Data leakage is the unauthorized transmission of data or information within an organization or from an organization to the external destination. The data stored in any device can be leaked in two ways; if the system is hacked or if the internal resources intentionally or unintentionally make the data public. Therefore, organizations should take measures to understand the sensitive data they hold, how it?s controlled, and how to prevent it from being leaked or compromised. So that purpose in this review data is preventing by using different technique of data leak prevention and detection