Numerous electronic systems store valuable intellectual property (IP) information inside non-volatile memories. In order to protect the integrity of such sensitive information from an unauthorized access or modification, encryption mechanisms are employed. From a reliability standpoint, such information can be vital to the system's functionality and thus, dedicated techniques are employed to detect possible reliability threats (e.g., transient faults in the memory content). In this paper we explore the capability of encryption mechanisms to guarantee protection from both unauthorized access and faults, while considering a Convolutional Neural Network application whose weights represent the valuable IP of the system. Experimental results show that it is possible to achieve very high fault detection rates, thus exploiting the benefits of security mechanisms for reliability purposes as well.

Improving the Fault Resilience of Neural Network Applications Through Security Mechanisms / Deligiannis, Nikolaos; Cantoro, Riccardo; SONZA REORDA, Matteo; Traiola, Marcello; Valea, Emanuele. - (2022), pp. 23-24. (Intervento presentato al convegno Dependable Systems and Networks nel 27-30 June 2022) [10.1109/DSN-S54099.2022.00017].

Improving the Fault Resilience of Neural Network Applications Through Security Mechanisms

Nikolaos Deligiannis;Riccardo Cantoro;Matteo Sonza Reorda;Emanuele Valea
2022

Abstract

Numerous electronic systems store valuable intellectual property (IP) information inside non-volatile memories. In order to protect the integrity of such sensitive information from an unauthorized access or modification, encryption mechanisms are employed. From a reliability standpoint, such information can be vital to the system's functionality and thus, dedicated techniques are employed to detect possible reliability threats (e.g., transient faults in the memory content). In this paper we explore the capability of encryption mechanisms to guarantee protection from both unauthorized access and faults, while considering a Convolutional Neural Network application whose weights represent the valuable IP of the system. Experimental results show that it is possible to achieve very high fault detection rates, thus exploiting the benefits of security mechanisms for reliability purposes as well.
File in questo prodotto:
File Dimensione Formato  
DSN_2022.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Pubblico - Tutti i diritti riservati
Dimensione 231.23 kB
Formato Adobe PDF
231.23 kB Adobe PDF Visualizza/Apri
Improving_the_Fault_Resilience_of_Neural_Network_Applications_Through_Security_Mechanisms.pdf

accesso riservato

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 742.4 kB
Formato Adobe PDF
742.4 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2970127