{"id":8643,"date":"2021-06-18T10:43:15","date_gmt":"2021-06-18T08:43:15","guid":{"rendered":"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/"},"modified":"2021-06-18T10:43:15","modified_gmt":"2021-06-18T08:43:15","slug":"privacy-security-for-artificial-intelligence","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/","title":{"rendered":"Privacy &amp; Security for Artificial Intelligence"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p>The rise of cloud computing technology led to a paradigm shift in technological services that enabled enterprises to delegate their data analytics tasks to third party (cloud) servers.\u00a0Machine\u00a0learning as a\u00a0service (MLaaS) provides stakeholders\u00a0with\u00a0the ease to perform machine learning tasks on a cloud platform.\u00a0<\/p><p>The\u00a0advantage of outsourcing these computationally-intensive operations unfortunately comes with high cost in terms of\u00a0<strong>privacy exposures<\/strong>. The goal is therefore to come up with customised\u00a0machine learning\u00a0algorithms\u00a0that would,\u00a0by design,\u00a0preserve the privacy of the processed data. Advanced cryptographic techniques,\u00a0such as fully homomorphic encryption or secure multi-party computation,\u00a0enable the execution of some operations over encrypted data and therefore can be considered as potential candidates for these algorithms.\u00a0However, these incur high computational and\/or communication costs for some operations.\u00a0<\/p><p>During this online event, Prof. Melek\u00a0\u00d6nen,\u00a0associate\u00a0professor in the Digital Security Department at EURECOM (Sophia-Antipolis, France),\u00a0will analyse the tension between\u00a0machine learning\u00a0techniques and relevant cryptographic tools.\u00a0Furthermore, she will give an\u00a0overview existing solutions addressing both privacy and security.<\/p><p>Join the event by following this link:\u00a0<a href=\"https:\/\/unilu.webex.com\/unilu\/j.php?MTID=ma76ec8727da2e913fd139157cd0e833b\" target=\"_blank\" title=\"https:\/\/unilu.webex.com\/unilu\/j.php?MTID=ma76ec8727da2e913fd139157cd0e833b\" rel=\"noopener\">https:\/\/unilu.webex.com\/unilu\/j.php?MTID=ma76ec8727da2e913fd139157cd0e833b<\/a>\u00a0(event password:\u00a04M5sTM9iPvW, from phones:\u00a046578694)<\/p><p><strong>Bio<\/strong>:<\/p><p><i>Melek \u00d6nen is an Associate Professor in the Digital Security Department at EURECOM (Sophia-Antipolis, France). Her research interests are applied cryptography, information security and privacy. She has worked in the design and the development of cryptographic protocols for various technologies including Big Data and the IoT. She holds a PhD in Computer Science from Ecole Nationale Sup\u00e9rieure des T\u00e9l\u00e9communications de Paris (ENST, 2005).<\/i><\/p><figure class=\"wp-block-dev4-reusable-blocks-image  object-fit--contain\">\n    \n<img decoding=\"async\" class=\"wp-block-image unilux-custom-image-block\"\n                alt=\"\"\n            src=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2024\/01\/onen_melek_1.jpg\"\n                    style=\"object-position: 50.00% 50.00%; font-family: &quot;object-fit: contain; object-position: 50.00% 50.00%;&quot;; aspect-ratio: 3\/4; object-fit: contain; width: 100%;\"\n        loading=\"lazy\"\n\/>    <\/figure><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>The rise of cloud computing technology led to a paradigm shift in technological services that enabled enterprises to delegate their data analytics tasks to third party (cloud) servers.\u00a0Machine\u00a0learning as a\u00a0service (MLaaS) provides stakeholders\u00a0with\u00a0the ease to perform machine learning tasks on a cloud platform.\u00a0The\u00a0advantage of outsourcing these computationally-intensive operations unfortunately comes with high cost in terms of\u00a0privacy exposures. The goal is therefore to come up with customised\u00a0machine learning\u00a0algorithms\u00a0that would,\u00a0by design,\u00a0preserve the privacy of the processed data. Advanced cryptographic techniques,\u00a0such as fully homomorphic encryption or secure multi-party computation,\u00a0enable the execution of some operations over encrypted data and therefore can be considered as potential candidates for these algorithms.\u00a0However, these incur high computational and\/or communication costs for some operations.\u00a0During this online event, Prof. Melek\u00a0\u00d6nen,\u00a0associate\u00a0professor in the Digital Security Department at EURECOM (Sophia-Antipolis, France),\u00a0will analyse the tension between\u00a0machine learning\u00a0techniques and relevant cryptographic tools.\u00a0Furthermore, she will give an\u00a0overview existing solutions addressing both privacy and security.Join the event by following this link:\u00a0https:\/\/unilu.webex.com\/unilu\/j.php?MTID=ma76ec8727da2e913fd139157cd0e833b\u00a0(event password:\u00a04M5sTM9iPvW, from phones:\u00a046578694)<\/p>\n","protected":false},"author":0,"featured_media":8644,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2021-07-01 16:00:00","event_end_date":"2021-07-01 18:00:00","event_speaker_name":"Professor Melek \u00d6nen","event_speaker_link":"","event_is_online":false,"event_location":"","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[],"events-type":[],"organisation":[184,226],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Privacy &amp; Security for Artificial Intelligence - Universit\u00e9 du Luxembourg<\/title>\n<meta name=\"description\" content=\"The rise of cloud computing technology led to a paradigm shift in technological services that enabled enterprises to delegate their data analytics tasks to third party (cloud) servers.\u00a0Machine\u00a0learning as a\u00a0service (MLaaS) provides stakeholders\u00a0with\u00a0the ease to perform machine learning tasks on a cloud platform.\u00a0The\u00a0advantage of outsourcing these computationally-intensive operations unfortunately comes with high cost in terms of\u00a0privacy exposures. The goal is therefore to come up with customised\u00a0machine learning\u00a0algorithms\u00a0that would,\u00a0by design,\u00a0preserve the privacy of the processed data. Advanced cryptographic techniques,\u00a0such as fully homomorphic encryption or secure multi-party computation,\u00a0enable the execution of some operations over encrypted data and therefore can be considered as potential candidates for these algorithms.\u00a0However, these incur high computational and\/or communication costs for some operations.\u00a0During this online event, Prof. Melek\u00a0\u00d6nen,\u00a0associate\u00a0professor in the Digital Security Department at EURECOM (Sophia-Antipolis, France),\u00a0will analyse the tension between\u00a0machine learning\u00a0techniques and relevant cryptographic tools.\u00a0Furthermore, she will give an\u00a0overview existing solutions addressing both privacy and security.Join the event by following this link:\u00a0https:\/\/unilu.webex.com\/unilu\/j.php?MTID=ma76ec8727da2e913fd139157cd0e833b\u00a0(event password:\u00a04M5sTM9iPvW, from phones:\u00a046578694)\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Privacy &amp; Security for Artificial Intelligence\" \/>\n<meta property=\"og:description\" content=\"The rise of cloud computing technology led to a paradigm shift in technological services that enabled enterprises to delegate their data analytics tasks to third party (cloud) servers.\u00a0Machine\u00a0learning as a\u00a0service (MLaaS) provides stakeholders\u00a0with\u00a0the ease to perform machine learning tasks on a cloud platform.\u00a0The\u00a0advantage of outsourcing these computationally-intensive operations unfortunately comes with high cost in terms of\u00a0privacy exposures. The goal is therefore to come up with customised\u00a0machine learning\u00a0algorithms\u00a0that would,\u00a0by design,\u00a0preserve the privacy of the processed data. Advanced cryptographic techniques,\u00a0such as fully homomorphic encryption or secure multi-party computation,\u00a0enable the execution of some operations over encrypted data and therefore can be considered as potential candidates for these algorithms.\u00a0However, these incur high computational and\/or communication costs for some operations.\u00a0During this online event, Prof. Melek\u00a0\u00d6nen,\u00a0associate\u00a0professor in the Digital Security Department at EURECOM (Sophia-Antipolis, France),\u00a0will analyse the tension between\u00a0machine learning\u00a0techniques and relevant cryptographic tools.\u00a0Furthermore, she will give an\u00a0overview existing solutions addressing both privacy and security.Join the event by following this link:\u00a0https:\/\/unilu.webex.com\/unilu\/j.php?MTID=ma76ec8727da2e913fd139157cd0e833b\u00a0(event password:\u00a04M5sTM9iPvW, from phones:\u00a046578694)\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/\" \/>\n<meta property=\"og:site_name\" content=\"UNI FR\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/uni.lu\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"2560\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/\",\"url\":\"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/\",\"name\":\"Privacy &amp; Security for Artificial Intelligence - Universit\u00e9 du Luxembourg\",\"isPartOf\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2021\/06\/privacy_security_for_artificial_intelligence.jpg\",\"datePublished\":\"2021-06-18T08:43:15+00:00\",\"dateModified\":\"2021-06-18T08:43:15+00:00\",\"description\":\"The rise of cloud computing technology led to a paradigm shift in technological services that enabled enterprises to delegate their data analytics tasks to third party (cloud) servers.\u00a0Machine\u00a0learning as a\u00a0service (MLaaS) provides stakeholders\u00a0with\u00a0the ease to perform machine learning tasks on a cloud platform.\u00a0The\u00a0advantage of outsourcing these computationally-intensive operations unfortunately comes with high cost in terms of\u00a0privacy exposures. 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Advanced cryptographic techniques,\u00a0such as fully homomorphic encryption or secure multi-party computation,\u00a0enable the execution of some operations over encrypted data and therefore can be considered as potential candidates for these algorithms.\u00a0However, these incur high computational and\/or communication costs for some operations.\u00a0During this online event, Prof. Melek\u00a0\u00d6nen,\u00a0associate\u00a0professor in the Digital Security Department at EURECOM (Sophia-Antipolis, France),\u00a0will analyse the tension between\u00a0machine learning\u00a0techniques and relevant cryptographic tools.\u00a0Furthermore, she will give an\u00a0overview existing solutions addressing both privacy and security.Join the event by following this link:\u00a0https:\/\/unilu.webex.com\/unilu\/j.php?MTID=ma76ec8727da2e913fd139157cd0e833b\u00a0(event password:\u00a04M5sTM9iPvW, from phones:\u00a046578694)","og_url":"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/","og_site_name":"UNI FR","article_publisher":"https:\/\/www.facebook.com\/uni.lu","og_image":[{"width":2560,"height":2560,"url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_misc":{"Dur\u00e9e de lecture estim\u00e9e":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/","url":"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/","name":"Privacy &amp; Security for Artificial Intelligence - Universit\u00e9 du Luxembourg","isPartOf":{"@id":"https:\/\/www.uni.lu\/fr\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/#primaryimage"},"image":{"@id":"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/#primaryimage"},"thumbnailUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2021\/06\/privacy_security_for_artificial_intelligence.jpg","datePublished":"2021-06-18T08:43:15+00:00","dateModified":"2021-06-18T08:43:15+00:00","description":"The rise of cloud computing technology led to a paradigm shift in technological services that enabled enterprises to delegate their data analytics tasks to third party (cloud) servers.\u00a0Machine\u00a0learning as a\u00a0service (MLaaS) provides stakeholders\u00a0with\u00a0the ease to perform machine learning tasks on a cloud platform.\u00a0The\u00a0advantage of outsourcing these computationally-intensive operations unfortunately comes with high cost in terms of\u00a0privacy exposures. The goal is therefore to come up with customised\u00a0machine learning\u00a0algorithms\u00a0that would,\u00a0by design,\u00a0preserve the privacy of the processed data. Advanced cryptographic techniques,\u00a0such as fully homomorphic encryption or secure multi-party computation,\u00a0enable the execution of some operations over encrypted data and therefore can be considered as potential candidates for these algorithms.\u00a0However, these incur high computational and\/or communication costs for some operations.\u00a0During this online event, Prof. Melek\u00a0\u00d6nen,\u00a0associate\u00a0professor in the Digital Security Department at EURECOM (Sophia-Antipolis, France),\u00a0will analyse the tension between\u00a0machine learning\u00a0techniques and relevant cryptographic tools.\u00a0Furthermore, she will give an\u00a0overview existing solutions addressing both privacy and security.Join the event by following this link:\u00a0https:\/\/unilu.webex.com\/unilu\/j.php?MTID=ma76ec8727da2e913fd139157cd0e833b\u00a0(event password:\u00a04M5sTM9iPvW, from phones:\u00a046578694)","breadcrumb":{"@id":"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/#primaryimage","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2021\/06\/privacy_security_for_artificial_intelligence.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2021\/06\/privacy_security_for_artificial_intelligence.jpg","width":800,"height":600},{"@type":"BreadcrumbList","@id":"https:\/\/www.uni.lu\/fr\/events\/privacy-security-for-artificial-intelligence\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.uni.lu\/fr\/"},{"@type":"ListItem","position":2,"name":"Events","item":"https:\/\/www.uni.lu\/fr\/events\/"},{"@type":"ListItem","position":3,"name":"Privacy &amp; Security for Artificial Intelligence"}]},{"@type":"WebSite","@id":"https:\/\/www.uni.lu\/fr\/#website","url":"https:\/\/www.uni.lu\/fr\/","name":"Uni.lu","description":"Universit\u00e9 du Luxembourg","publisher":{"@id":"https:\/\/www.uni.lu\/fr\/#organization"},"alternateName":"Universit\u00e9 du Luxembourg","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.uni.lu\/fr\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/www.uni.lu\/fr\/#organization","name":"Universit\u00e9 du Luxembourg","alternateName":"Uni.lu","url":"https:\/\/www.uni.lu\/fr\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","width":2560,"height":2560,"caption":"Universit\u00e9 du Luxembourg"},"image":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/04\/24120552\/20200609-Belval-Campus_Henri-Goergen-23.jpg","sameAs":["https:\/\/www.facebook.com\/uni.lu","https:\/\/www.linkedin.com\/school\/university-of-luxembourg\/","https:\/\/www.instagram.com\/uni.lu","https:\/\/www.youtube.com\/@uni_lu","https:\/\/en.wikipedia.org\/wiki\/University_of_Luxembourg"],"email":"communication@uni.lu","telephone":"+352 46 66 44 1","address":{"@type":"PostalAddress","streetAddress":"2, place de l\u2019Universit\u00e9","addressLocality":"Esch-sur-Alzette","postalCode":"4365","addressCountry":"LU"},"description":"Universit\u00e9 du Luxembourg"}]}},"_links":{"self":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events\/8643"}],"collection":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events"}],"about":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/types\/events"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/comments?post=8643"}],"version-history":[{"count":0,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events\/8643\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/media\/8644"}],"wp:attachment":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/media?parent=8643"}],"wp:term":[{"taxonomy":"events-topic","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events-topic?post=8643"},{"taxonomy":"events-type","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events-type?post=8643"},{"taxonomy":"organisation","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/organisation?post=8643"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}