LDR | | 00000nam u2200205 4500 |
001 | | 000000431510 |
005 | | 20200224102049 |
008 | | 200131s2019 ||||||||||||||||| ||eng d |
020 | |
▼a 9781088332771 |
035 | |
▼a (MiAaPQ)AAI13904841 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
082 | 0 |
▼a 004 |
100 | 1 |
▼a Kotsogiannis, Ios. |
245 | 10 |
▼a Query Answering in Multi-relational Databases under Differential Privacy. |
260 | |
▼a [S.l.]:
▼b Duke University.,
▼c 2019. |
260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
300 | |
▼a 147 p. |
500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
500 | |
▼a Advisor: Machanavajjhala, Ashwin. |
502 | 1 |
▼a Thesis (Ph.D.)--Duke University, 2019. |
506 | |
▼a This item must not be sold to any third party vendors. |
520 | |
▼a Data collection has become a staple of both our digital and ``off-line'' activities. Government agencies, medical institutions, Internet companies, and academic institutions are among the main actors that collect and store users' data. Analysis and sharing of this data is paramount in our increasingly data-driven world.Data sharing provides a large positive societal value |
590 | |
▼a School code: 0066. |
650 | 4 |
▼a Computer science. |
690 | |
▼a 0984 |
710 | 20 |
▼a Duke University.
▼b Computer Science. |
773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0066 |
791 | |
▼a Ph.D. |
792 | |
▼a 2019 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15492565
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
980 | |
▼a 202002
▼f 2020 |
990 | |
▼a ***1008102 |
991 | |
▼a E-BOOK |