Privacy-preserving machine learning:
Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthe...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Shelter Island, NY
Manning Publications
[2023]
|
| Schlagworte: | |
| Zusammenfassung: | Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation Privacy-enhancing technologies for data mining and database applications Compressive privacy for machine learning Privacy Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You'll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. |
| Beschreibung: | xx, 311 Seiten Illustrationen, Diagramme 24 cm |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV049027877 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | t | ||
| 008 | 230629s2023 a||| |||| 00||| eng d | ||
| 015 | |a GBC1J3396 |2 dnb | ||
| 020 | |z 9781617298042 |9 978-1-61729-804-2 | ||
| 035 | |a (OCoLC)1389175688 | ||
| 035 | |a (DE-599)BVBBV049027877 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-1050 | ||
| 084 | |a ST 277 |0 (DE-625)143643: |2 rvk | ||
| 100 | 1 | |a Chang, J. Morris |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Privacy-preserving machine learning |c J. Morris Chang, Di Zhuang, Dumindu Samaraweera |
| 264 | 1 | |a Shelter Island, NY |b Manning Publications |c [2023] | |
| 300 | |a xx, 311 Seiten |b Illustrationen, Diagramme |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 505 | 8 | |a Basics of privacy-preserving machine learning with differential privacy -- Local differential privacy and synthetic data generation -- Building privacy-assured machine learning applications | |
| 520 | |a Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation Privacy-enhancing technologies for data mining and database applications Compressive privacy for machine learning Privacy Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You'll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. | ||
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Computer networks / Security measures | |
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 4 | |a Réseaux d'ordinateurs / Sécurité / Mesures | |
| 650 | 7 | |a Computer networks / Security measures |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 700 | 1 | |a Zhuang, Di |e Verfasser |4 aut | |
| 700 | 1 | |a Samaraweera, Dumindu |e Verfasser |4 aut | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-034290638 | ||
Datensatz im Suchindex
| _version_ | 1804185309954441216 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Chang, J. Morris Zhuang, Di Samaraweera, Dumindu |
| author_facet | Chang, J. Morris Zhuang, Di Samaraweera, Dumindu |
| author_role | aut aut aut |
| author_sort | Chang, J. Morris |
| author_variant | j m c jm jmc d z dz d s ds |
| building | Verbundindex |
| bvnumber | BV049027877 |
| classification_rvk | ST 277 |
| contents | Basics of privacy-preserving machine learning with differential privacy -- Local differential privacy and synthetic data generation -- Building privacy-assured machine learning applications |
| ctrlnum | (OCoLC)1389175688 (DE-599)BVBBV049027877 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02357nam a2200397 c 4500</leader><controlfield tag="001">BV049027877</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">230629s2023 a||| |||| 00||| eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">GBC1J3396</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9781617298042</subfield><subfield code="9">978-1-61729-804-2</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1389175688</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV049027877</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-1050</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 277</subfield><subfield code="0">(DE-625)143643:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chang, J. Morris</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Privacy-preserving machine learning</subfield><subfield code="c">J. Morris Chang, Di Zhuang, Dumindu Samaraweera</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Shelter Island, NY</subfield><subfield code="b">Manning Publications</subfield><subfield code="c">[2023]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xx, 311 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">24 cm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Basics of privacy-preserving machine learning with differential privacy -- Local differential privacy and synthetic data generation -- Building privacy-assured machine learning applications</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation Privacy-enhancing technologies for data mining and database applications Compressive privacy for machine learning Privacy Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You'll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. </subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer networks / Security measures</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Réseaux d'ordinateurs / Sécurité / Mesures</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Computer networks / Security measures</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">fast</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhuang, Di</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Samaraweera, Dumindu</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034290638</subfield></datafield></record></collection> |
| id | DE-604.BV049027877 |
| illustrated | Illustrated |
| index_date | 2024-07-03T22:15:40Z |
| indexdate | 2024-07-10T09:53:12Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034290638 |
| oclc_num | 1389175688 |
| open_access_boolean | |
| owner | DE-1050 |
| owner_facet | DE-1050 |
| physical | xx, 311 Seiten Illustrationen, Diagramme 24 cm |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Manning Publications |
| record_format | marc |
| spelling | Chang, J. Morris Verfasser aut Privacy-preserving machine learning J. Morris Chang, Di Zhuang, Dumindu Samaraweera Shelter Island, NY Manning Publications [2023] xx, 311 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Basics of privacy-preserving machine learning with differential privacy -- Local differential privacy and synthetic data generation -- Building privacy-assured machine learning applications Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation Privacy-enhancing technologies for data mining and database applications Compressive privacy for machine learning Privacy Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You'll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. Machine learning Computer networks / Security measures Apprentissage automatique Réseaux d'ordinateurs / Sécurité / Mesures Computer networks / Security measures fast Machine learning fast Zhuang, Di Verfasser aut Samaraweera, Dumindu Verfasser aut |
| spellingShingle | Chang, J. Morris Zhuang, Di Samaraweera, Dumindu Privacy-preserving machine learning Basics of privacy-preserving machine learning with differential privacy -- Local differential privacy and synthetic data generation -- Building privacy-assured machine learning applications Machine learning Computer networks / Security measures Apprentissage automatique Réseaux d'ordinateurs / Sécurité / Mesures Computer networks / Security measures fast Machine learning fast |
| title | Privacy-preserving machine learning |
| title_auth | Privacy-preserving machine learning |
| title_exact_search | Privacy-preserving machine learning |
| title_exact_search_txtP | Privacy-preserving machine learning |
| title_full | Privacy-preserving machine learning J. Morris Chang, Di Zhuang, Dumindu Samaraweera |
| title_fullStr | Privacy-preserving machine learning J. Morris Chang, Di Zhuang, Dumindu Samaraweera |
| title_full_unstemmed | Privacy-preserving machine learning J. Morris Chang, Di Zhuang, Dumindu Samaraweera |
| title_short | Privacy-preserving machine learning |
| title_sort | privacy preserving machine learning |
| topic | Machine learning Computer networks / Security measures Apprentissage automatique Réseaux d'ordinateurs / Sécurité / Mesures Computer networks / Security measures fast Machine learning fast |
| topic_facet | Machine learning Computer networks / Security measures Apprentissage automatique Réseaux d'ordinateurs / Sécurité / Mesures |
| work_keys_str_mv | AT changjmorris privacypreservingmachinelearning AT zhuangdi privacypreservingmachinelearning AT samaraweeradumindu privacypreservingmachinelearning |