Seabed is a project to provide analytics over encrypted Big Data. The challenge is to develop fast yet secure cryptographic techniques that support a suite of applications such as Business Intelligence tools and large-scale Machine Learning frameworks. Towards this, we have developed two novel cryptographic techniques:a high-performance, additive symmetric homomorphic scheme (ASHE) and\u00a0Splayed ASHE (SPLASHE), an encryption scheme that thwarts frequency analysis-based attacks.<\/p>\n
We have built Seabed into Apache Spark and we show that we can perform analytics over encrypted data with an average overhead of only 25%, where previous techniques had overheads ranging between 10x\u00a0 and 100x. Going forward, we are looking at building Machine Learning models over encrypted data.<\/p>\n
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Seabed is a project to provide analytics over encrypted Big Data. The challenge is to develop fast yet secure cryptographic techniques that support a suite of applications such as Business Intelligence tools and large-scale Machine Learning frameworks. Towards this, we have developed two novel cryptographic techniques:a high-performance, additive symmetric homomorphic scheme (ASHE) and\u00a0Splayed ASHE (SPLASHE), […]<\/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":"","footnotes":""},"research-area":[13561,13556,13558,13547],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-212078","msr-project","type-msr-project","status-publish","hentry","msr-research-area-algorithms","msr-research-area-artificial-intelligence","msr-research-area-security-privacy-cryptography","msr-research-area-systems-and-networking","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"12\/1\/2015","related-publications":[292613],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","value":"nichandr","display_name":"Nishanth Chandran","author_link":"Nishanth Chandran<\/a>","is_active":false,"user_id":33084,"last_first":"Chandran, Nishanth","people_section":0,"alias":"nichandr"},{"type":"user_nicename","value":"ramjee","display_name":"Ramachandran Ramjee","author_link":"Ramachandran Ramjee<\/a>","is_active":false,"user_id":33337,"last_first":"Ramjee, Ramachandran","people_section":0,"alias":"ramjee"}],"msr_research_lab":[199562],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/212078"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/212078\/revisions"}],"predecessor-version":[{"id":390896,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/212078\/revisions\/390896"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=212078"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=212078"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=212078"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=212078"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=212078"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}