{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117807"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117807","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Manufacturing espionage: from the front office to the factory floor","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Adhikari, Anku"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Winslett, Marianne S","Gunter, Carl A","Smaragdis, Paris","Sumei, Sun"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Security","Privacy","Manufacturing","Data Architecture","Industry 4.0","Digital Threads","Big Data","Iot","Blockchain","Worm","Side Channel","Data Exfiltration","Covert Channel","Acoustic","Vibrations","Vulnerabilities","Countermeasures"],"languages":["en","eng"],"rights":["Copyright 2022 Anku Adhikari"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117807","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Winslett, Marianne S","Gunter, Carl A","Smaragdis, Paris","Sumei, Sun"]},{"key":"dc:creator","label":"Author","values":["Adhikari, Anku"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-11-30"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Security","Privacy","Manufacturing","Data Architecture","Industry 4.0","Digital Threads","Big Data","Iot","Blockchain","Worm","Side Channel","Data Exfiltration","Covert Channel","Acoustic","Vibrations","Vulnerabilities","Countermeasures"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Anku Adhikari"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117807"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Anku Adhikari, accepted the attached license on 2022-11-29 at 22:25.","The student, Anku Adhikari, submitted this Dissertation for approval on 2022-11-29 at 22:32.","This Dissertation was approved for publication on 2022-11-30 at 12:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18688 on 2023-04-12 at 07:36:09","This dissertation identifies and addresses new data security problems for manufacturing environments and their associated commercial office spaces. The work includes novel attacks and defenses for the factory floor and front office, as well as an architecture for securely sharing data all along the manufacturing supply chain. Security needs for storing and sharing manufacturing data. Even back in 2015 when digitization was in its infancy, manufacturers already produced more data than any other sector of industry. The subsequent push toward Manufacturing 4.0, with its smart factories driven by big data, has intensified the need to extract value from manufacturing data that can drive innovation and efficiency improvements. One crucial component of this is the ability to share data securely, starting at the factory floor, continuing on up throughout the supply chain and across all the enterprises in a manufacturing federation. Any weak link in this chain of organizations constitutes a threat that can have a major negative impact for organizations all along the chain. This dissertation explains the reasons for the move towards integration of information about high-value manufactured products, and investigates how data can be retrieved securely from machines on the factory floor and converted into snapshots of data packets called “digital threads” that give fine-grained insight into the processes happening on the factory floor at any point of time. The challenge is to support secure extraction, processing, storage and secure sharing for huge amounts of manufacturing data in a trustless environment with multiple players. The dissertation outlines several key threats to digital threads that have not been fully addressed in previous work on securing provenance information, and proposes digital-threads-as-a-service (DTaaS) as a potential way to ameliorate several of the open issues. The dissertation proposes a hybrid data architecture framework to provide integrity and immutability support for managing digital threads as they are generated, stored and appropriately shared all along the supply chain, from factory floor to end user. Side-channel vulnerabilities on factory floors. Even before the data leaves the factory floor for intended sharing and storage, the sounds made by machines on a factory floor can leak data that gives away manufacturing secrets. This dissertation explores pathways for intellectual property theft during the design and machining process of a product, and demonstrates the vulnerability through acoustic and electromagnetic side-channel attacks that can reconstruct the product design and manufacturing process for simple objects with high accuracy and precision. Novel eavesdropping pathways in office spaces. A front office that is air-gapped and secured against traditional attacks can still be vulnerable to data exfiltration attacks. This dissertation demonstrates vulnerabilities that exist in almost all commercial buildings today as a side effect of building code requirements. In particular, the dissertation exposes a novel covert channel attack that exploits the building’s own physical infrastructure to leak data throughout the building and beyond. Based on acoustic transducers, this novel audio attack supports wide bandwidth eavesdropping channels. The dissertation also proposes and experimentally evaluates several methods to defend against such an attack."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Manufacturing espionage: from the front office to the factory floor"]}]}],"canonical_facts":{"dc:contributor":["Winslett, Marianne S","Gunter, Carl A","Smaragdis, Paris","Sumei, Sun"],"dc:creator":["Adhikari, Anku"],"dc:date":["2022-12","2022-11-30"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Anku Adhikari, accepted the attached license on 2022-11-29 at 22:25.","The student, Anku Adhikari, submitted this Dissertation for approval on 2022-11-29 at 22:32.","This Dissertation was approved for publication on 2022-11-30 at 12:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18688 on 2023-04-12 at 07:36:09","This dissertation identifies and addresses new data security problems for manufacturing environments and their associated commercial office spaces. The work includes novel attacks and defenses for the factory floor and front office, as well as an architecture for securely sharing data all along the manufacturing supply chain. Security needs for storing and sharing manufacturing data. Even back in 2015 when digitization was in its infancy, manufacturers already produced more data than any other sector of industry. The subsequent push toward Manufacturing 4.0, with its smart factories driven by big data, has intensified the need to extract value from manufacturing data that can drive innovation and efficiency improvements. One crucial component of this is the ability to share data securely, starting at the factory floor, continuing on up throughout the supply chain and across all the enterprises in a manufacturing federation. Any weak link in this chain of organizations constitutes a threat that can have a major negative impact for organizations all along the chain. This dissertation explains the reasons for the move towards integration of information about high-value manufactured products, and investigates how data can be retrieved securely from machines on the factory floor and converted into snapshots of data packets called “digital threads” that give fine-grained insight into the processes happening on the factory floor at any point of time. The challenge is to support secure extraction, processing, storage and secure sharing for huge amounts of manufacturing data in a trustless environment with multiple players. The dissertation outlines several key threats to digital threads that have not been fully addressed in previous work on securing provenance information, and proposes digital-threads-as-a-service (DTaaS) as a potential way to ameliorate several of the open issues. The dissertation proposes a hybrid data architecture framework to provide integrity and immutability support for managing digital threads as they are generated, stored and appropriately shared all along the supply chain, from factory floor to end user. Side-channel vulnerabilities on factory floors. Even before the data leaves the factory floor for intended sharing and storage, the sounds made by machines on a factory floor can leak data that gives away manufacturing secrets. This dissertation explores pathways for intellectual property theft during the design and machining process of a product, and demonstrates the vulnerability through acoustic and electromagnetic side-channel attacks that can reconstruct the product design and manufacturing process for simple objects with high accuracy and precision. Novel eavesdropping pathways in office spaces. A front office that is air-gapped and secured against traditional attacks can still be vulnerable to data exfiltration attacks. This dissertation demonstrates vulnerabilities that exist in almost all commercial buildings today as a side effect of building code requirements. In particular, the dissertation exposes a novel covert channel attack that exploits the building’s own physical infrastructure to leak data throughout the building and beyond. Based on acoustic transducers, this novel audio attack supports wide bandwidth eavesdropping channels. The dissertation also proposes and experimentally evaluates several methods to defend against such an attack."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117807"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Anku Adhikari"],"dc:subject":["Security","Privacy","Manufacturing","Data Architecture","Industry 4.0","Digital Threads","Big Data","Iot","Blockchain","Worm","Side Channel","Data Exfiltration","Covert Channel","Acoustic","Vibrations","Vulnerabilities","Countermeasures"],"dc:title":["Manufacturing espionage: from the front office to the factory floor"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}