BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Pacific/Honolulu
BEGIN:STANDARD
DTSTART:19470608T023000
TZOFFSETFROM:-1130
TZOFFSETTO:-1000
TZNAME:HST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20221105T001121Z
UID:4C40B6C4-788D-468C-B9B5-697260C6ABA4
DTSTART;TZID=Pacific/Honolulu:20221101T160000
DTEND;TZID=Pacific/Honolulu:20221101T170000
DESCRIPTION:Today we face an explosion of systems from health monitoring to
  national security infrastructure that generate and collect vast data dail
 y. Increasingly\, these systems use machine learning methods for intellige
 nt decisions\, prone to cyber-security attacks. So\, we ask how data priva
 cy should be protected in a world where data is gathered and shared with i
 ncreasing speed and ingenuity. This presentation will describe several pri
 vacy techniques for streaming data protection\, frameworks for machine lea
 rning\, and privacy attacks. We will share results using real-world datase
 ts and ORNL testbed and describe best practices. The talk concludes with a
  brief discussion of present open challenges in privacy-preserving algorit
 hms and how the research findings can be transferred to industry.\n\nSpeak
 er(s): Dr. Olivera Kotevska\, \n\nAgenda: \nThis is an in-person meeting w
 ith a live remote presenter and Q&amp;A.\n\n45 min presentation\, 15 min Q&amp;A\,
  followed by networking.\n\nEntrepreneurs Sandbox\, 643 Ilalo St.\, Honolu
 lu\, Hawaii\, United States\, 96813 
LOCATION:Entrepreneurs Sandbox\, 643 Ilalo St.\, Honolulu\, Hawaii\, United
  States\, 96813 
ORGANIZER:eugene.chang@ieee.org
SEQUENCE:7
SUMMARY:Privacy algorithms: Research Practice and Transfer to Industry
URL;VALUE=URI:https://events.vtools.ieee.org/m/323892
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Today we face an explosion of systems from
  health monitoring to national security infrastructure that generate and c
 ollect vast data daily. Increasingly\, these systems use machine learning 
 methods for intelligent decisions\, prone to cyber-security attacks. So\, 
 we ask how data privacy should be protected in a world where data is gathe
 red and shared with increasing speed and ingenuity. This presentation will
  describe several privacy techniques for streaming data protection\, frame
 works for machine learning\, and privacy attacks. We will share results us
 ing real-world datasets and ORNL testbed and describe best practices. The 
 talk concludes with a brief discussion of present open challenges in priva
 cy-preserving algorithms and how the research findings can be transferred 
 to industry.&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p&gt;This is an in
 -person meeting with a live remote presenter and Q&amp;amp\;A.&lt;/p&gt;\n&lt;p&gt;45 min 
 presentation\, 15 min Q&amp;amp\;A\, followed by networking.&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/
 p&gt;
END:VEVENT
END:VCALENDAR