{"id":755317,"date":"2021-06-16T15:23:43","date_gmt":"2021-06-16T22:23:43","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=755317"},"modified":"2025-08-01T13:29:56","modified_gmt":"2025-08-01T20:29:56","slug":"megamind-a-platform-for-security-privacy-extensions-for-voice-assistants","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/megamind-a-platform-for-security-privacy-extensions-for-voice-assistants\/","title":{"rendered":"MegaMind: a platform for security & privacy extensions for voice assistants"},"content":{"rendered":"<p>Voice assistants raise serious security and privacy concerns because they use always-on microphones in sensitive locations (e.g., inside a home) and send audio recordings to the cloud for processing. The cloud transcribes these recordings and interprets them as user requests, and sometimes even shares these requests with third-party services. These steps may result in unintended or malicious voice data leaks and in unauthorized actions, such as a purchase. This paper presents MegaMind, a novel extensible platform that lets a user deploy security and privacy extensions locally on their voice assistant. MegaMind&#8217;s extensions interpose on requests before sending them to the cloud and on responses before delivering them to the user. MegaMind&#8217;s programming model enables writing powerful extensions with ease, such as one for secure conversations. Additionally, MegaMind protects against malicious extensions by providing two important guarantees, namely permission enforcement and non-interference. We implement MegaMind and integrate it with Amazon Alexa Service SDK. Our evaluation shows that MegaMind achieves a small conversation latency on platforms with adequate compute power, such as a Raspberry Pi 4 and an x86-based laptop.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Voice assistants raise serious security and privacy concerns because they use always-on microphones in sensitive locations (e.g., inside a home) and send audio recordings to the cloud for processing. The cloud transcribes these recordings and interprets them as user requests, and sometimes even shares these requests with third-party services. These steps may result in unintended [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":null,"msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2021-6-23","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":null,"footnotes":""},"msr-research-highlight":[],"research-area":[13563],"msr-publication-type":[193715],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[246691],"msr-conference":[],"msr-journal":[270065],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-755317","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us","msr-field-of-study-computer-science"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-6-23","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"doi","viewUrl":"false","id":"false","title":"https:\/\/doi.org\/10.1145\/3458864.3467962","label_id":"243106","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/dblp.org\/rec\/conf\/mobisys\/TalebiSSW21.html","label_id":"243109","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3458864.3467962","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"S. 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