?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Quantifying+Web-Search+Privacy&rft.creator=Gervais%2C+Arthur&rft.creator=Shokri%2C+Reza&rft.creator=Singla%2C+Adish&rft.creator=Capkun%2C+Srdjan&rft.creator=Lenders%2C+Vincent&rft.description=Web+search+queries+reveal+extensive+information+about+users%E2%80%99%0D%0Apersonal+lives+to+the+search+engines+and+Internet+eavesdroppers.+Obfuscating+search+queries+through+adding+dummy%0D%0Aqueries+is+a+practical+and+user-centric+protection+mechanism%0D%0Ato+hide+users%E2%80%99+search+intentions+and+interests.+Despite+few%0D%0Asuch+obfuscation+methods+and+tools%2C+there+is+no+generic%0D%0Aquantitative+methodology+for+evaluating+users%E2%80%99+web-search%0D%0Aprivacy.+In+this+paper%2C+we+provide+such+a+methodology.+We%0D%0Aformalize+adversary%E2%80%99s+background+knowledge+and+attacks%2C%0D%0Athe+users%E2%80%99+privacy+objectives%2C+and+the+algorithms+to+evaluate+effectiveness+of+query+obfuscation+mechanisms.+We%0D%0Abuild+upon+machine-learning+algorithms+to+learn+the+linkability+between+user+queries.+This+encompasses+the+adversary%E2%80%99s+knowledge+about+the+obfuscation+mechanism+and+the%0D%0Ausers%E2%80%99+web-search+behavior.+Then%2C+we+quantify+privacy+of%0D%0Ausers+with+respect+to+linkage+attacks.+Our+generic+attack+can%0D%0Arun+against+users+for+which+the+adversary+does+not+have+any%0D%0Abackground+knowledge%2C+as+well+as+for+the+cases+where+some%0D%0Aprior+queries+from+the+target+users+are+already+observed.+We%0D%0Aquantify+privacy+at+the+query+level+(the+link+between+user%E2%80%99s%0D%0Aqueries)+and+the+semantic+level+(user%E2%80%99s+topics+of+interest).+We%0D%0Adesign+a+generic+tool+that+can+be+used+for+evaluating+generic%0D%0Aobfuscation+mechanisms%2C+and+users+with+different+web+search%0D%0Abehavior.+To+illustrate+our+approach+in+practice%2C+we+analyze%0D%0Aand+compare+privacy+of+users+for+two+example+obfuscation%0D%0Amechanisms+on+a+set+of+real+web-search+logs.&rft.subject=Web+Search%3B+Privacy%3B+Obfuscation%3B+Quantification+Framework%3B+Query+Privacy%3B+Semantic+Privacy%3B+Machine+Learning&rft.publisher=Association+for+Computing+Machinery+(ACM)&rft.date=2014-11-03&rft.type=Proceedings+paper&rft.language=eng&rft.source=+++++In%3A++Proceedings+of+the+2014+ACM+SIGSAC+Conference+on+Computer+and+Communications+Security.++(pp.+pp.+966-977).++Association+for+Computing+Machinery+(ACM)%3A+Scottsdale%2C+AZ%2C+USA.+(2014)+++++&rft.format=text&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F10182351%2F1%2Fccs_gervais.pdf&rft.identifier=https%3A%2F%2Fdiscovery.ucl.ac.uk%2Fid%2Feprint%2F10182351%2F&rft.rights=open