Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and inmemory computing(IMC),but there is a rising interest in using memristive technologies for security application...Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and inmemory computing(IMC),but there is a rising interest in using memristive technologies for security applications in the era of internet of things(IoT).In this review article,for achieving secure hardware systems in IoT,lowpower design techniques based on emerging memristive technology for hardware security primitives/systems are presented.By reviewing the state-of-the-art in three highlighted memristive application areas,i.e.memristive non-volatile memory,memristive reconfigurable logic computing and memristive artificial intelligent computing,their application-level impacts on the novel implementations of secret key generation,crypto functions and machine learning attacks are explored,respectively.For the low-power security applications in IoT,it is essential to understand how to best realize cryptographic circuitry using memristive circuitries,and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security.This review article aims to help researchers to explore security solutions,to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.展开更多
Artificial intelligence(AI)algorithms achieve outstanding results in many applicationdomains such as computer vision and natural language processing The performance ofAl models is the outcome of complex and costly mod...Artificial intelligence(AI)algorithms achieve outstanding results in many applicationdomains such as computer vision and natural language processing The performance ofAl models is the outcome of complex and costly model architecture design and trainingprocesses.Hence,it is paramount for model owners to protect their AI models frompiracy-model cloning,illegitimate distribution and use.IP protection mechanisms havebeen applied to Al models,and in particular to deep neural networks,to verify themodel ownership.State-of-the-art AI model ownership protection techniques have beensurveyed.The pros and cons of Al model ownership protection have been reported.The majonity of previous works are focused on watermarking,while more advancedmethods such fingerprinting and attestation are promising but not yet explored indepth.This study has been concluded by discussing possible research directions in thearea.展开更多
基金supported by the DFG(German Research Foundation)Priority Program Nano Security,Project MemCrypto(Projektnummer 439827659/funding id DU 1896/2–1,PO 1220/15–1)the funding by the Fraunhofer Internal Programs under Grant No.Attract 600768。
文摘Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and inmemory computing(IMC),but there is a rising interest in using memristive technologies for security applications in the era of internet of things(IoT).In this review article,for achieving secure hardware systems in IoT,lowpower design techniques based on emerging memristive technology for hardware security primitives/systems are presented.By reviewing the state-of-the-art in three highlighted memristive application areas,i.e.memristive non-volatile memory,memristive reconfigurable logic computing and memristive artificial intelligent computing,their application-level impacts on the novel implementations of secret key generation,crypto functions and machine learning attacks are explored,respectively.For the low-power security applications in IoT,it is essential to understand how to best realize cryptographic circuitry using memristive circuitries,and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security.This review article aims to help researchers to explore security solutions,to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.
基金supported by the European Union Horizon 2020 research and innovation program under CPSoSAware project(grant no.871738)by Science Foundation Ireland,grant no.12/RC/2289-P2,Insight Centre for Data Analytics。
文摘Artificial intelligence(AI)algorithms achieve outstanding results in many applicationdomains such as computer vision and natural language processing The performance ofAl models is the outcome of complex and costly model architecture design and trainingprocesses.Hence,it is paramount for model owners to protect their AI models frompiracy-model cloning,illegitimate distribution and use.IP protection mechanisms havebeen applied to Al models,and in particular to deep neural networks,to verify themodel ownership.State-of-the-art AI model ownership protection techniques have beensurveyed.The pros and cons of Al model ownership protection have been reported.The majonity of previous works are focused on watermarking,while more advancedmethods such fingerprinting and attestation are promising but not yet explored indepth.This study has been concluded by discussing possible research directions in thearea.
