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AI has been making some major strides in the computing world in recent years. But that also means they have become increasingly vulnerable to security concerns. Just by examining the power usage patterns or signatures during operations, one may able to gain access to sensitive information housed by a computer system. And in AI, machine learning algorithms are more prone to such attacks. The same algorithms are employed in smart home devices, cars to identify different forms of images and sounds that are embedded with specialized computing chips.

These chips rely on using neural networks, instead of a cloud computing server located in a data center miles away. Due to such physical proximity, the neural networks can perform computations, at a faster rate, with minimal delay. This also makes it simple for hackers to reverse-engineer the chips inner workings using a method known as differential power analysis (DPA). Thereby, it is a warning threat for the Internet of Things/edge devices because of their power signatures or electromagnetic radiation signage. If leaked, the neural model, including weights, biases, and hyper-parameters, can violate data privacy and intellectual property rights.

Recently a team of researchers of North Carolina State University presented a preprint paper at the 2020 IEEE International Symposium on Hardware Oriented Security and Trust in San Jose, California. The paper mentions about the DPA framework to neural-network classiers. First, it shows DPA attacks during inference to extract the secret model parameters such as weights and biases of a neural network. Second, it proposes the rst countermeasures against these attacks by augmenting masking. The resulting design uses novel masked components such as masked adder trees for fully connected layers and masked Rectier Linear Units for activation functions. The team is led by Aydin Aysu, an assistant professor of electrical and computer engineering at North Carolina State University in Raleigh.

While DPA attacks have been successful against targets like the cryptographic algorithms that safeguard digital information and the smart chips found in ATM cards or credit cards, the team observes neural networks as possible targets, with perhaps even more profitable payoffs for the hackers or rival competitors. They can further unleash adversarial machine learning attacks that can confuse the existing neural network

The team focused on common and simple binarized neural networks (an efcient network for IoT/edge devices with binary weights and activation values) that are adept at doing computations with less computing resources. They began by demonstrating how power consumption measurements can be exploited to reveal the secret weight and values that help determine a neural networks computations. Using random known inputs, for multiple numbers of time, the adversary computes the corresponding power activity on an intermediate estimate of power patterns linked with the secret weight values of BNN, in a highly-parallelized hardware implementation.

Then the team designed a countermeasure to secure the neural network against such an attack via masking (an algorithm-level defense that can produce resilient designs independent of the implementation technology). This is done by splitting intermediate computations into two randomized shares that are different each time the neural network runs the same intermediate computation. This prevents an attacker from using a single intermediate computation to analyze different power consumption patterns. While the process requires tuning for protecting specific machine learning models, they can be executed on any form of computer chip that runs on a neural network, viz., Field Programmable Gate Arrays (FPGA), and Application-specific Integrated Circuits (ASIC). Under this defense technique, a binarized neural network requires the hypothetical adversary to perform 100,000 sets of power consumption measurements instead of just 200.

However, there are certain main concerns involved in the masking technique. During initial masking, the neural networks performance dropped by 50 percent and needed nearly double the computing area on the FPGA chip. Second, the team expressed the possibility of attackers avoid the basic masking defense by analyzing multiple intermediate computations instead of a single computation, thus leading to a computational arms race where they are split into further shares. Adding more security to them can be time-consuming.

Despite this, we still need active countermeasures against DPA attacks. Machine Learning (ML) is a critical new target with several motivating scenarios to keep the internal ML model secret. While Aysu explains that research is far from done, his research is supported by both the U.S. National Science Foundation and the Semiconductor Research Corporations Global Research Collaboration. He anticipates receiving funding to continue this work for another five years and hopes to enlist more Ph.D. students interested in the effort.

Interest in hardware security is increasing because, at the end of the day, the hardware is the root of trust, Aysu says. And if the root of trust is gone, then all the security defenses at other abstraction levels will fail.

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New Research Claims to Have Found a Solution to Machine Learning Attacks - Analytics Insight

Pune, May 18, 2020 (GLOBE NEWSWIRE) -- The global patent analytics market size is predicted to USD 1,668.4 million by 2027, exhibiting a CAGR of 12.4% during the forecast period. The increasing advancement and integration of machine learning, artificial intelligence, and the neural network by enterprises will have a positive impact on the market during the forecast period. Moreover, the growing needs of companies to protect intellectual assets will bolster healthy growth of the market in the forthcoming years, states Fortune Business Insights in a report, titled Patent Analytics Market Size, Share and Industry Analysis, By Component (Solutions and Services), By Services (Patent Landscapes/White Space Analysis, Patent Strategy and Management, Patent Valuation, Patent Support, Patent Analytics, and Others), By Enterprise Size (Large Enterprises, Small & Medium Enterprises), By Industry (IT and Telecommunications, Healthcare, Banking, Financial Services and Insurance (BFSI), Automotive, Media and Entertainment, Food and Beverages and, Others), and Regional Forecast, 2020-2027 the market size stood at USD 657.9 million in 2019. The rapid adoption of the Intellectual Property (IP) system to retain an innovation-based advantage in business will aid the expansion of the market.

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The emergence of COVID-19 has brought the world to a standstill. We understand that this health crisis has brought an unprecedented impact on businesses across industries. However, this too shall pass. Rising support from governments and several companies can help in the fight against this highly contagious disease. There are some industries that are struggling and some are thriving. Overall, almost every sector is anticipated to be impacted by the pandemic.

We are taking continuous efforts to help your business sustain and grow during COVID-19 pandemics. Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.

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Market Driver:

Integration of Artificial Intelligence to Improve Market Prospects

The implementation of artificial intelligence technology for analyzing patent data will support the expansion of the market. AI-based semantic search uses an artificial neural network to enhance patent discovery by improving accuracy and efficiency. For instance, in February 2018, PatSeer announced the unveiling of ReleSense, an AI-driven NLP engine. The engine utilizes 12 million+ semantic rules to gain from publically available patents, scientific journals, clinical trials, and associated data sources. ReleSense with its wide range of AI-driven capabilities offers search from classification, via APIs and predictive-analytics for apt IP solutions. The growing application of AI for domain-specific analytics will augur well for the market in the forthcoming years. Furthermore, the growing government initiatives to promote patent filing activities will boost the patent analytics market share during the forecast period. For instance, the Government of India introduced a new scheme named Innovative/ Creative India, to aware people of the patents and IP laws and support patent analytics. In addition, the growing preferment for language model and neural network intelligence for accurate, deep, and complete data insights will encourage the market.

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Regional Analysis:

Implementation of Advanced Technologies to Promote Growth in North America

The market in North America stood at USD 209.2 million and is expected to grow rapidly during the forecast period owing to the presence of major companies in the US such as IBM Corporation, Amazon.Com, Inc. The implementation of advanced technologies including IoT, big data, and artificial intelligence by major companies will aid growth in the region.

Considering this the U.S. is expected to showcase a higher growth in the patent filing. As per the World Intellectual Property, in 2018, the U.S. filed 230,085 patent applications across several domains. Asia Pacific is predicted to witness tremendous growth during the forecast period. The growth is attributed to China, which accounts for a major share in the global patent filings. According to WIPO, intellectual property (IP) office in China had accounted for 46.6% global share in patent registration, in 2018. The growing government initiatives concerning patents and IP laws in India will significantly enable speedy growth in Asia Pacific.

Key Development:

March 2018: Ipan GmbH announced its collaboration with Patentsight, Corsearch, and Uppdragshuset for the introduction of an open IP platform named IP-x-change platform. The platform enables prior art search, automatic data verification tools, smart docketing tools integrated in real-time to optimize IP management solution.

List of Key Companies Operating in the Patent Analytics Market are:

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Detailed Table of Content

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Have a Look at Related Research Insights:

Intellectual Property Software Market Size, Share and Global Trend By Deployment (On-premises & Cloud-based solutions), By Services (Development & Implementation Services, Consulting Services, Maintenance & Support Services), By Applications (Patent Management, Trademark Management and others), By Industry Vertical (Healthcare, Electronics and others) and Geography Forecast till 2025

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Patent Analytics Market to Reach USD 1,668.4 Million by 2027; Integration of Machine Learning and Artificial Intelligence to Spur Business...