Anonymising CMC corpora: A reasonable way to share · 2013. 2. 19. · italiano, mi primer nombre, Peimikà, es tailandés y significa " dueña del amor ", mientras mi según nombre,

Anonymising CMC corpora: A reasonable way to share

Christophe ReffaySTEF (ENS Cachan)

IFÉ (ENS Lyon)

14 feb. 2013 Dortmund'2013 - [email protected] 2

Outline• Introduction:

– The corpus example– Anonymisation objective and purpose– Anonymisation principles and hypotheses– The Mulce “anony” XML schema

• Anonymisation process– Marking process– Finding new forms– Replacement process

• Conclusion? Suggestions? =>Perspectives


The corpus (1/2)• A GALANET session:

– Based on Inter-Comprehension principles:• Write mainly in your mother language• Read and try to understand others

– Discussion forum and collaborative production– For teenagers (~16 years old)

Multilingual interaction messages Many typos & grammatical errors Use of internet language (smiley,…)


The corpus (2/2)• Galanet Session 2011-2012:

“Nômades...nomadi...nómades... des langues”

• 4 teams : Italy, Brazil, France & Spain• During 3.5 months, • 103 teenagers, 83 authors wrote…

915 Messages containing (message body)• Volume: 47 740 tokens, 217 477 characters• Lexicon: 9 655 distinct types


The objective is to share!

But anonymisation is a hard work (by hand)– The corpus may be enormous– Subtleties: homonyms & synonyms

Personal data are not sharable

Anonymisation… the solution?

Need a software to support


Anonymisation purpose • Hide personal information systematicallysystematically

– Names (first names, last names, usernames…)– Identifiers (Passport, National Student Number, …)– Locations (city, street, address, coordinates)– Institution/Workplace (school, sport club, firm, …)– Contact references (e-mail, mobile, MSN, skype,

twitter, telephone/fax)– Explicit references (URL of homepages, blogs)– Social media usernames (facebook, MySpace, Hi5,

Soundcloud, Badoo, Bebo, Friendster, Netlog, …)• Maintaining text coherence and consistency


Personal data: examples• {(f331s2970m2)2011-11-30T19:24 Gabibr Re: Quelques

informations ... answers SandrineD (f331s2970m1)} “Eu amo a língua Francesa! Quem sabe falar francês me adiconem no meu FACEBOOK;) J'aime parler français! Qui peut parler français? M'ajouter dans FACEBOOK;) Nom: GABRIELA MEDEIROS.”

• {(f333s3016m2)2011-12-27T09:25 Miche Re: Les stéréotypesculinaires answers SandrineD (f333s3016m1)} “inviate i vostridocumenti alla mia mail [email protected] grazie!!!;)”

• {(f330s2914m8)2011-10-22T19:52 PBS Re: Por que me chamoassim?! answers Eloandrade (f330s2914m1)} “Yo me llamoPeimikà Bibiana. Como mi madre es tailandesa y mi padre es italiano, mi primer nombre, Peimikà, es tailandés y significa " dueñadel amor ", mientras mi según nombre, Bibiana, es italiano y procede del etrusco " vibius " que significa " vida ". Me gusta muchotener dos nombres (en Italia es más usual tener un nombre) y sobre todo estoy orgullosa de los orígenes diferentes que tienen y que hacen mi nombre aún más particular (además Peimikà no es muy difundido en tampoco en Tailandia y tampoco Bibiana en Italia”


Just google it!


Peimikà Bibiana… google search (2)


Anonymisation Principles

1. All identified tokens must be (computationally) marked even if not modified by a replacement token.

2. Any reference (e.g.: name, institution or location) may be imprecise enough to encompass several hundreds people.

Original lexical form Replacement formReplacedby

Mark

Once anonymised, no participant may be identifiable by an external person


Anonymisation• Before:

{(f330s2880m3)2011-10-17T08:22 KellyM Re: Qui sommes- nous? answers CarlaN (f330s2880m1)}

Bonjour, je m'appelle Kellly. J'ai 16 ans, je suis une élève en 1ère S dans le lycée Rosa Luxemburg à Canet, non loin de Perpignan…

• After:{(f330s2880m3)2011-10-17T08:22 FLG01 Re: Qui sommes- nous? answers ILG02 (f330s2880m1)}

Bonjour, je m'appelle Kittty*. J'ai 16 ans, je suis une élève en 1ère S dans le lycée Margherita Duras* à Aigues-Vives*, non loin de Perpignan…

Before After

Anonymisation

process

Looking for a reasonable


Hypotheses• A fully automated method does not exist

for any corpora• Some decisions have to be taken

by the researcher, not by the software• Accuracy of the method will be achieved

only for a given context (ex: Galanet)• “Named entities” do not occur randomly

Let’s find the regularities Interactively with the expert: the researcher


Concepts manipulated

Institution,Participant,

Public person,Relative,Street,

City…

Existing objects Named entities Lexical forms

Name,Surname,

Username,First name,Last name,Addresses,

Tel. number,MSN…

Pedro,KellyM,Eli, Elô,Kelly,

Bergamo, Canet,Rosa Luxembourg,

0609785643,

Real world CorpusReference

MarienkircheMarienkircheMarien-kircheMaria-kirche

Marienkirche in Lübeck von Osten

Marienkirche in Dortmund


Anonymisation process

Corpus toanonymise

Corpus with marked

Entities

Named entitiestransformation tableInitial list of

participants,usernames,institution…

Process/RulesDiscovering new forms

MarkingProcess

AnonymisedCorpus

ReplacementProcess


Transformation table: example

Synonyms: the same entity has different forms

=≠

Homonyms: the same form refers to different entities



Corpus toanonymise

Corpus with marked

Entities




MarkingProcess

AnonymisedCorpus

ReplacementProcess


Marking one form: Example (Kelly)A- List of all occurrences (within their context) with a concordancer


Marking one form: Example (Kelly)

+

B- Update the transformation table (ex: add Public person Gene Kelly)


Marking one form: Example (Kelly)

C- Associate each occurrence to the appropriate entity

(=> In the corpus: Surround the occurrence by XML tags)

Last name, Normal form, unchangedrefers to the public person Gene Kelly

First name, Normal form, to be changedrefers to the participant KellyM



Corpus toanonymise

Corpus with marked

Entities




MarkingProcess

AnonymisedCorpus

ReplacementProcess


Detecting new forms: 2 strategies

• Lexical rules: similar forms – Eli -> Elô Ely ELY Seli– Gabriela -> GABRIELA– José -> Jose

• Context rules: Similar context– First names: “mi chiamo …”, “accord avec …”– Cities: “Soy de …”, “vivo en …”, “j’habite à …”


1st Strategy: Lexical variation rules

adriana Alexia Antonhy baptiste Cleisa Elô Ely ELY Seli Louise MAnuel Federiac fran Fran GABRIELA guillem iñigo Jacqueline jean Jose Kellly Leo léo MariAna mary May Miche michelina moni olalla oleguer

Adriana Alèxia Anthony Baptiste Cleissa Eli…Elouise Emmanuel Federica Ferran Gabriela Guillem Iñigo Jaqueline Jean José Kelly Léo Mariana Mary Michela Monica Olalla Oleguer

103Knownforms

31New

forms


Lexical variations

110oleguerOleguer

110olallaOlalla

2132moniMonica

2132michelinaMichela

222MicheMichela

111MayMary

1110maryMary

2110MariAnaMariana

110léoLéo

111LeoLéo

111KelllyKelly

111JoseJosé

110jeanJean

111JacquelineJaqueline

110iñigoIñigo

110guillemGuillem

770GABRIELAGabriela

Add/Sup/InvaccentsCasedistancedistanceNewKnown

nb differencesLevensteinLevenstein

ExactUPPER


2nd Strategy : Context rules

103 Known first names (Adrià, …, Veronica)

145 contexts: Left/RightTotal: more than 250 tested rules

15 good new formsAntonhy Belle Bet Christine FedeFederiac Kellly Leo Line Maria May Peimikà Regina fran jean léo

47 rules approved



Corpus toanonymise

Corpus with marked

Entities




MarkingProcess

AnonymisedCorpus

ReplacementProcess


Replacing process• Before:

{(f330s2880m3)2011-10-17T08:22 KellyM Re: Qui sommes- nous? answers CarlaN (f330s2880m1)}

Bonjour, je m'appelle Kellly. J'ai 16 ans, je suis une élève en 1ère S dans le lycée Rosa Luxemburg à Canet, non loin de Perpignan…

• After:{(f330s2880m3)2011-10-17T08:22 FLG01 Re: Qui sommes- nous? answers ILG02 (f330s2880m1)}

Bonjour, je m'appelle Kittty*. J'ai 16 ans, je suis une élève en 1ère S dans le lycée Margherita Duras* à Aigues-Vives*, non loin de Perpignan…


The Mulce anony.xsd schemahttp://lrl-diffusion.univ-bpclermont.fr/mulce/metadata/mce-schemas/mce_anony.xsd

<actordesignation actorref = “{actor_code}” person = “real/fictitious” process = “{process_mark}”><firstname type = “initial/abbreviated/shortname/complete”

correct = “exact/modified/wrong”modified = “true/false”>Replacement form</firstname>

{May be some separators like: spaces, linefeed, paragraph, or «,.;:!?=+ -/\~*_["]#(')&@$% », others?}

<surname type = “initial/abbreviated/complete” correct = “exact/modified/wrong” modified = “true/false”>Replacement form</surname>

{May be some separators}<lastname type = “initial/abbreviated/complete” correct =

“exact/modified/wrong” modified = “true/false”>Replacement form</lastname>

</actordesignation>


Anonymisation• Before:Bonjour, je m'appelle Kellly. J'ai 16 ans, …

• After:Bonjour, je m'appelle<actordesignation actorref="FLG01" person=“real” process=“WBCMC_CR130214”><firstname type=“complete” correct=“wrong”modified=“true”>Kittty</firstname></actordesignation>. J'ai 16 ans, …

Before After


Overall method and tools1. Define a process/algorithm for anonymisation2. Confront hypotheses to a first corpus

– Using existing tools (Excel, TXM/Calico, Notepad++)– Doing many work by hand (with automation in mind)– Facing/solving/avoiding problems– Evaluating/Suggesting (new) hypotheses

3. Discuss the result with the original researcher4. Verify on a second (bigger corpus)5. Co-develop the tool within the research

community


Conclusion

1. An anonymisation method:– For any language– not relying on any external information (dictionnary)

2. Confront hypotheses to a first corpus– 47 rules approved for first names => 15 new forms– 103 first names => 31 existing derivations– Anonymisation not 100% auto => confirmed

3. Anonymisation possible in a world with Google?– Use Google to get the frequency of a named entities!

Strategy for systematic anonymisation of multi-lingual interaction corpora.

C. Reffay1, F.-M. Blondel1, E. Giguet21 STEF – ENS-Cachan / IFÉ – ENS-Lyon

2 GREYC, Université Caen Basse-Normandie, CNRS

Acknowledgement: Most of the material used in this presentation had been produced for the communication of the “Inter-Comprehension” conference, held in Grenoble in June 2012

Vielen Dank!

More precisely


New forms discovering: 2 strategies

103 Known first names (Adrià, …, Veronica)

LexicalRules

ContextRules

317 candidates 145 contexts: Left/RightLeft: One form: 75 => 13780 occ.Left: 2 forms seq.: 123 => 1700 occ.Total: more than 250 tested rules

5 common 31 good new forms1 relative new: Maria30 public names

67 Tests

180 common words20 username200 Easy

34 frequent words16 known50 Auto

47 rules approved

15 good new forms


Contexts of 145 occ. of 103 first names(using TXM, case insensitive)


The corpus lexicon

• A list of (lexical forms ► Frequence)– de ►1015– que ► 965– la ► 673– …– porque ► 48– …– Addams ► 1

9655 unique forms


Who is concerned?

« Les applications informatiques à des fins pédagogiques et éducatives mobilisent des données permettant d’identifier directement mais aussi indirectement les personnes physiques. Une attention particulière doit être portée sur la collecte de données sensibles ainsi que sur les procédés d’anonymisation des données. »

(Mallet-Poujol 2004: p 21)

For more information, see the European Commission Directive (95/46/EC)

Translation


Who is concerned?

« Learning and teaching software involves data that could be used to identify directly or even indirectly physical persons. We have to pay attention particularly on sensitive data collection and on the process of data anonymisation. »

(Mallet-Poujol 2004: p 21)

For more information, see the European Commission Directive (95/46/EC)


Legal context (95/46/EC)

• (Art7) Member States shall provide that personal datamay be processed only if: the data subject has unambiguously given his consent;…

• (Art8) Member States shall prohibit the processing of personal data revealing sensitive information (racial or ethnic origin, political opinions, religious or philosophical beliefs, trade-union membership, and the processing of data concerning health or sex life)

• (Art8) […] Inform the data subject on:– The identity of the controller of the data collection,– The purposes of the processing – The recipients or categories of recipients of the data,– The existence of the right of access to and the right to rectify the

data concerning him


Text coherence and consistency• {(f330s2914m11)2011-10-20T16:43 M_Cavalcanti Re: Por que me chamo

assim?! Answers Eloandrade (f330s2914m1)} “aaah, o meu é uma homenagem a uma de minhas tias e minha avó que se chamam Ana e ao resto de minhas tias que se chamam Maria. Daí, Mariana:)”

• {(f330s2914m10)-2011-10-20T21:06 Eloandrade Re: Por que me chamo assim?! answers Eloandrade (f330s2914m1)} “Gostei da criatividade da sua mãe MariAna! Rsrsrs”

• {(f330s2914m3)2011-10-28T00:54 LineCosta Re: Por que me chamo assim?! answers Eloandrade (f330s2914m1)} “Ah meu nome é em homenagem a Jacqueline Kennedy, esposa do ex- presidente dos EUA, e também porque sempre foi um dos nomes preferidos do meu pai.: D”

• {(f330s2914m18)2011-10-19T20:36 Eloandrade Re: Por que me chamo assim?! answers Eloandrade (f330s2914m1)} “Bem, minha mãe queria que meu nome começasse com a letra E (como o dela!), um certo dia ela viu o nome de uma atriz brasileira chamada Louise Cardoso. Gostou do " Louise ", mas queria com a letra E, então ficou " Elouise "! Só depois, quando eu cresci é que descobri que meu nome era de origem francesa.. Hahaha”


TXM: http://textometrie.ens-lyon.fr/


Named entities

A named entity is a lexical formidentifying a precise object (first/last name, communication ref., city, institution, etc.)

Examples:Names: Christophe, Blondel, Giguet, Paris, Communication ref.: 0678600614, …Location: Grenoble, Paris, Parigi, …Institution: ENS Cachan, CNRS, …


Managing named entities

• Homonyms refer to different objects– In the corpus we have 2 participants named “Guillem”:

The same first name refers to different persons.– In “Gene Kelly”, Kelly = public person last name– in “Galdric, Kelly et Antonhy”, it’s a participant first name

• Different synonyms refer to the same object– Kellly & Kelly, – Anthony & Antonhy, – Elô & Elouise


Referring to global entities


Find Nei/nei with a concordancer

All occurrences refer to the Italian common word “nei”


Another example

• {(f330s2914m5)2011-10-23T21:52 CR_MartinsRe: Por que me chamo assim?! answers Eloandrade (f330s2914m1)} “Meu nome éCleissa Regina, Cleissa porque minha mãe viu na tv uma repórter chamada Cleisa e achou parecido com o nome dela, Cléia e Regina porque o nome do meu pai é Reginaldo. Assim como a PBS gosto muito de ter 2 nomes e Cleissa é bem raro, nunca conheci ninguém chamado assim.”


Peimikà Bibiana… a unique case? No! Let’s try Cleissa Regina…


How to detect new forms?

• Lexical rules (look for similar forms): – Ignoring accents (ex: José, Jose)– Ignoring case (ex: José, jose, JOSÉ, …)– Levenstein distance between 2 forms: number of

extra/missing/inversion of characters– For word size <5 : Dist<=1– For word size >=5 : Dist<=2

• Context rules: (ex: “mi chiamo …”, “merci …”)


Lexical variations 1/2

222FranFerran

2132franFerran

222FederiacFederica

2242MAnuelEmmanuel

1121LouiseElouise

1121SeliEli

121ELYEli

11ElyEli

111ElôEli

111CleisaCleissa

110baptisteBaptiste

222AntonhyAnthony

111AlexiaAlèxia

110adrianaAdriana



ExactUPPER


Lexical variations 2/2

110oleguerOleguer

110olallaOlalla

2132moniMonica

2132michelinaMichela

222MicheMichela

111MayMary

1110maryMary

2110MariAnaMariana

110léoLéo

111LeoLéo

111KelllyKelly

111JoseJosé

110jeanJean

111JacquelineJaqueline

110iñigoIñigo

110guillemGuillem

770GABRIELAGabriela



ExactUPPER


Some good context rules (1/3)

100%Maria112chamam <F>

50%12tampoco <F>

1

2

11

New

33%13choix <F>

33%13raison <F>

33%13appel <F>

100%Peimikà23llamo <F>

33%13dicho <F>

25%14Hombre <F>

20%15equipe <F>

40%25soy <F>

44%Belle, léo19Merci <F>

17%16Ciao <F>

29%May17Cara <F>

56%Kelly49appelle <F>

20%210sou <F>

AccuracyNew forms detectedKnownTotalContext


Some good context rules (2/3)

100%111diu el <F>

100%Peimikà112nombre, <F>

1

11

1New

100%11dit el <F>

100%11<F> a dit

50%12suis avec <F>

100%jean01je m’appel <F>

25%28<F>, je

100%22<F>, j’habite

25%28<F>, ho

100%22moi c’est <F>

67%23meu nome é <F>

100%Line23Concordo com a <F>

56%Bet49Accord avec <F>

100%55je m’appelle <F>

15%Fede862{BOM} <F>,



Generic context rules

12

1New

33%13<F> e <Known>

33%13<Known> e <F>

100%Federiac23<F> et <Known>

67%Antonhy, Leo26<Known> et <F>

100%11<F> i <Known>

33%13<Known> i <F>

20%Regina215<F>, <Known>


Documents

Anonymising CMC corpora: A reasonable way to share · 2013. 2. 19. · italiano, mi primer nombre, Peimikà, es tailandés y significa " dueña del amor ", mientras mi según nombre,