BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
BEGIN:DAYLIGHT
DTSTART:20250330T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20241027T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20250321T121513Z
UID:5C07C970-0E75-4ED4-8285-4ACCB3959C16
DTSTART;TZID=Europe/Stockholm:20250311T151500
DTEND;TZID=Europe/Stockholm:20250311T163000
DESCRIPTION:In today's world\, our diverse information is collected through
  various channels and utilized for a range of purposes\, including statist
 ical inference and the development of machine learning models. However\, p
 rivacy threats continue to emerge\, revealing that sensitive personal info
 rmation can be inferred from statistics or machine learning models. In thi
 s tutorial\, we introduce differential privacy\, a representative privacy 
 protection metric\, and explore its applications in machine learning and s
 tatistical inference. Furthermore\, focusing on distribution estimation\, 
 a fundamental problem in statistical inference\, we will discuss how class
 ical results from combinatorics can be leveraged to develop communication-
 efficient differential privacy techniques.\n\nCo-sponsored by: Lund Univer
 sity\, Lund\, Sweden\n\nSpeaker(s): Prof. Si-Hyeon Lee\, \n\nRoom: E:2311\
 , Bldg: E Building\, Lund University\, Klas Anshelms väg 10\, Lund\, Skan
 e lan\, Sweden
LOCATION:Room: E:2311\, Bldg: E Building\, Lund University\, Klas Anshelms 
 väg 10\, Lund\, Skane lan\, Sweden
ORGANIZER:michael.lentmaier@eit.lth.se
SEQUENCE:29
SUMMARY:IEEE ITSoc Distinguished Lecturer Tour: Prof. Si-Hyeon Lee\, Privac
 y-Preserving Data Utilization with Differential Privacy
URL;VALUE=URI:https://events.vtools.ieee.org/m/472641
X-ALT-DESC:Description: <br /><div class="x_elementToProof" style="font-fam
 ily: Calibri\, Arial\, Helvetica\, sans-serif\; font-size: 12pt\; color: #
 000000\;">\n<p>In today's world\, our diverse information is collected thr
 ough various channels and utilized for a range of purposes\, including sta
 tistical inference and the development of machine learning models. However
 \, privacy threats continue to emerge\, revealing that sensitive personal 
 information can be inferred from statistics or machine learning models. In
  this tutorial\, we introduce differential privacy\, a representative priv
 acy protection metric\, and explore its applications in machine learning a
 nd statistical inference. Furthermore\, focusing on distribution estimatio
 n\, a fundamental problem in statistical inference\, we will discuss how c
 lassical results from combinatorics can be leveraged to develop communicat
 ion-efficient differential privacy techniques.</p>\n</div>
END:VEVENT
END:VCALENDAR