{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/127018"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/127018","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Secure analog-to-digital conversion against power side-channel attack","abstract":"At the interface between analog circuits and a digital processor, an ADC can create a critical hardware security loophole. By exploiting the power side-channel leakage of the ADC, an attacker can expose the private signal chain data. Having recognized the security threat, this thesis explores both aspects of the SAR ADC power side-channel attack (PSA): attack method and its countermeasure. Firstly, this thesis proposes two neural-network-based SAR ADC PSA methods based on multi-layer perceptron net-works (MLP-PSA) and convolutional neural networks (CNN-PSA). When applied to a SAR ADC without PSA protection, the proposed attack methods decode the power supply current waveforms of the SAR ADC into the corresponding A/D conversion results with very high accuracy, demonstrating themselves as powerful ADC PSA methods. Secondly, this thesis proposes a current-equalizer-based SAR ADC PSA countermeasure. A 12-bit, 1.25MS/s prototype SAR ADC is implemented in 65nm CMOS technology for the proof-of-concept. With the proposed PSA countermeasure, the prototype SAR ADC demonstrated a strong PSA-resistance against MLP-PSA. Due to the second-order power side-channel leakage sources of a current equalizer, the prototype SAR ADC showed weaker PSA-resistance against CNN-PSA, but generally protected a significant portion of the information from the attack.","abstract_html":"At the interface between analog circuits and a digital processor, an ADC can create a critical hardware security loophole. By exploiting the power side-channel leakage of the ADC, an attacker can expose the private signal chain data. Having recognized the security threat, this thesis explores both aspects of the SAR ADC power side-channel attack (PSA): attack method and its countermeasure. Firstly, this thesis proposes two neural-network-based SAR ADC PSA methods based on multi-layer perceptron net-works (MLP-PSA) and convolutional neural networks (CNN-PSA). When applied to a SAR ADC without PSA protection, the proposed attack methods decode the power supply current waveforms of the SAR ADC into the corresponding A/D conversion results with very high accuracy, demonstrating themselves as powerful ADC PSA methods. Secondly, this thesis proposes a current-equalizer-based SAR ADC PSA countermeasure. A 12-bit, 1.25MS/s prototype SAR ADC is implemented in 65nm CMOS technology for the proof-of-concept. With the proposed PSA countermeasure, the prototype SAR ADC demonstrated a strong PSA-resistance against MLP-PSA. Due to the second-order power side-channel leakage sources of a current equalizer, the prototype SAR ADC showed weaker PSA-resistance against CNN-PSA, but generally protected a significant portion of the information from the attack.","abstract_has_math":false,"creators":["Jeong, Taehoon."],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Anantha P. Chandrakasan and Hae-Seung Lee."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:21:21Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/127018","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Anantha P. Chandrakasan and Hae-Seung Lee."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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By exploiting the power side-channel leakage of the ADC, an attacker can expose the private signal chain data. Having recognized the security threat, this thesis explores both aspects of the SAR ADC power side-channel attack (PSA): attack method and its countermeasure. Firstly, this thesis proposes two neural-network-based SAR ADC PSA methods based on multi-layer perceptron net-works (MLP-PSA) and convolutional neural networks (CNN-PSA). When applied to a SAR ADC without PSA protection, the proposed attack methods decode the power supply current waveforms of the SAR ADC into the corresponding A/D conversion results with very high accuracy, demonstrating themselves as powerful ADC PSA methods. Secondly, this thesis proposes a current-equalizer-based SAR ADC PSA countermeasure. A 12-bit, 1.25MS/s prototype SAR ADC is implemented in 65nm CMOS technology for the proof-of-concept. With the proposed PSA countermeasure, the prototype SAR ADC demonstrated a strong PSA-resistance against MLP-PSA. Due to the second-order power side-channel leakage sources of a current equalizer, the prototype SAR ADC showed weaker PSA-resistance against CNN-PSA, but generally protected a significant portion of the information from the attack."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph. D."]},{"key":"dc:title","label":"Title","values":["Secure analog-to-digital conversion against power side-channel attack"]}]}],"canonical_facts":{"dc:contributor.advisor":["Anantha P. Chandrakasan and Hae-Seung Lee."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. 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Firstly, this thesis proposes two neural-network-based SAR ADC PSA methods based on multi-layer perceptron net-works (MLP-PSA) and convolutional neural networks (CNN-PSA). When applied to a SAR ADC without PSA protection, the proposed attack methods decode the power supply current waveforms of the SAR ADC into the corresponding A/D conversion results with very high accuracy, demonstrating themselves as powerful ADC PSA methods. Secondly, this thesis proposes a current-equalizer-based SAR ADC PSA countermeasure. A 12-bit, 1.25MS/s prototype SAR ADC is implemented in 65nm CMOS technology for the proof-of-concept. With the proposed PSA countermeasure, the prototype SAR ADC demonstrated a strong PSA-resistance against MLP-PSA. Due to the second-order power side-channel leakage sources of a current equalizer, the prototype SAR ADC showed weaker PSA-resistance against CNN-PSA, but generally protected a significant portion of the information from the attack."],"dc:description.degree":["Ph. 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