Do you pay for Privacy in Online learning?

Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory. Differential privacy, instead, is the most widely used statistical concept of privacy in the machine learning community. It is thus clear that defining learning problems that are online different...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-10
Main Authors: Sanyal, Amartya, Ramponi, Giorgia
Format: Article
Language:English
Subjects:
Learning theory
Machine learning
Privacy
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Sanyal, Amartya
Ramponi, Giorgia
description Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory. Differential privacy, instead, is the most widely used statistical concept of privacy in the machine learning community. It is thus clear that defining learning problems that are online differentially privately learnable is of great interest. In this paper, we pose the question on if the two problems are equivalent from a learning perspective, i.e., is privacy for free in the online learning framework?
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2723659206</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2723659206</sourcerecordid><originalsourceid>FETCH-proquest_journals_27236592063</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mTQcslXqMwvVShIrFRIyy9SCCjKLEtMrlTIzFPwz8vJzEtVyElNLMrLzEu352FgTUvMKU7lhdLcDMpuriHOHroFRfmFpanFJfFZ-aVFeUCpeCNzI2MzU0sjAzNj4lQBAMKYMC0</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2723659206</pqid></control><display><type>article</type><title>Do you pay for Privacy in Online learning?</title><source>Publicly Available Content Database</source><creator>Sanyal, Amartya ; Ramponi, Giorgia</creator><creatorcontrib>Sanyal, Amartya ; Ramponi, Giorgia</creatorcontrib><description>Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory. Differential privacy, instead, is the most widely used statistical concept of privacy in the machine learning community. It is thus clear that defining learning problems that are online differentially privately learnable is of great interest. In this paper, we pose the question on if the two problems are equivalent from a learning perspective, i.e., is privacy for free in the online learning framework?</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Learning theory ; Machine learning ; Privacy</subject><ispartof>arXiv.org, 2022-10</ispartof><rights>2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2723659206?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>776,780,25731,36989,44566</link.rule.ids></links><search><creatorcontrib>Sanyal, Amartya</creatorcontrib><creatorcontrib>Ramponi, Giorgia</creatorcontrib><title>Do you pay for Privacy in Online learning?</title><title>arXiv.org</title><description>Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory. Differential privacy, instead, is the most widely used statistical concept of privacy in the machine learning community. It is thus clear that defining learning problems that are online differentially privately learnable is of great interest. In this paper, we pose the question on if the two problems are equivalent from a learning perspective, i.e., is privacy for free in the online learning framework?</description><subject>Learning theory</subject><subject>Machine learning</subject><subject>Privacy</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mTQcslXqMwvVShIrFRIyy9SCCjKLEtMrlTIzFPwz8vJzEtVyElNLMrLzEu352FgTUvMKU7lhdLcDMpuriHOHroFRfmFpanFJfFZ-aVFeUCpeCNzI2MzU0sjAzNj4lQBAMKYMC0</recordid><startdate>20221010</startdate><enddate>20221010</enddate><creator>Sanyal, Amartya</creator><creator>Ramponi, Giorgia</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20221010</creationdate><title>Do you pay for Privacy in Online learning?</title><author>Sanyal, Amartya ; Ramponi, Giorgia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_27236592063</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Learning theory</topic><topic>Machine learning</topic><topic>Privacy</topic><toplevel>online_resources</toplevel><creatorcontrib>Sanyal, Amartya</creatorcontrib><creatorcontrib>Ramponi, Giorgia</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sanyal, Amartya</au><au>Ramponi, Giorgia</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Do you pay for Privacy in Online learning?</atitle><jtitle>arXiv.org</jtitle><date>2022-10-10</date><risdate>2022</risdate><eissn>2331-8422</eissn><abstract>Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory. Differential privacy, instead, is the most widely used statistical concept of privacy in the machine learning community. It is thus clear that defining learning problems that are online differentially privately learnable is of great interest. In this paper, we pose the question on if the two problems are equivalent from a learning perspective, i.e., is privacy for free in the online learning framework?</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2022-10
issn 2331-8422
language eng
recordid cdi_proquest_journals_2723659206
source Publicly Available Content Database
subjects Learning theory
Machine learning
Privacy
title Do you pay for Privacy in Online learning?
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T07%3A02%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Do%20you%20pay%20for%20Privacy%20in%20Online%20learning?&rft.jtitle=arXiv.org&rft.au=Sanyal,%20Amartya&rft.date=2022-10-10&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2723659206%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_27236592063%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2723659206&rft_id=info:pmid/&rfr_iscdi=true