Nghiên cứu phát triển một số giao thức tính tổng bảo mật hiệu quả trong mô hình dữ liệu phân tán đầy đủ và ứng dụng

MINISTRY OF EDUCATION VIETNAM ACADEMY OF SCIENCE
AND TRAINING AND TECHNOLOGY

GRADUATE UNIVERSITY OF SCIENCE AND TECHNOLOGY

Vu Duy Hien

DEVELOPING EFFICIENT AND SECURE MULTI-PARTY
SUM COMPUTATION PROTOCOLS
AND THEIR APPLICATIONS

DISSERTATION ON INFORMATION SYSTEM

Hanoi – 2024

BỘ GIÁO DỤC VIỆN HÀN LÂM KHOA HỌC
VÀ ĐÀO TẠO VÀ CÔNG NGHỆ VIỆT NAM

HỌC VIỆN KHOA HỌC VÀ CÔNGNGHỆ

Vũ Duy Hiến

NGHIÊN CỨU PHÁT TRIỂN MỘT SỐ GIAO THỨC TÍNH
TỔNG BẢO MẬT HIỆU QUẢ TRONG MƠ HÌNH DỮ LIỆU

PHÂN TÁN ĐẦY ĐỦ VÀ ỨNG DỤNG

LUẬN ÁN TIẾN SĨ NGÀNH HỆ THỐNG THÔNG TIN

Hà Nội – 2024

BỘ GIÁO DỤC VIỆN HÀN LÂM KHOA HỌC
VÀ ĐÀO TẠO VÀ CÔNG NGHỆ VIỆT NAM

HỌC VIỆN KHOA HỌC VÀ CÔNGNGHỆ

Vũ Duy Hiến

NGHIÊN CỨU PHÁT TRIỂN MỘT SỐ GIAO THỨC TÍNH
TỔNG BẢO MẬT HIỆU QUẢ TRONG MƠ HÌNH DỮ LIỆU

PHÂN TÁN ĐẦY ĐỦ VÀ ỨNG DỤNG

LUẬN ÁN TIẾN SĨ NGÀNH HỆ THỐNG THÔNG TIN
Mã số: 9 48 01 04

Xác nhận củaHọc Người hướng dẫn 1 Người hướng dẫn 2
việnKhoahọcvàCôngng (Ký, ghi rõ họ tên) (Ký, ghi rõ họ tên)
hệ

GS. TSKH. Hồ Tú Bảo PGS. TS. Lương Thế Dũng

Hà Nội - 2024

1

PLEDGE

I promise that the thesis: ”Developing efficient and secure multi-

partysumcomputationprotocolsandtheirapplications”ismyoriginalresearchworkunde

rthe guidance of the academic supervisors. All contents of the thesis

were

writtenbasedonpapersandarticlespublishedindistinguishedinternationalconferen

cesandjournals published by the reputed publishers. The source of the

references

inthisthesisareexplitlycited.Myresearchresultswerepublishedjointlywithotherauth

ors andwereagreeduponbytheco-

authorswhenincludedinthethesis.Newresultsanddiscussionspresentedinthethesis

areperfectlyhonestandtheyhavenotyetpublishedbyanyotherauthorsbeyondmypublic

ations.Thisthesishasbeenfinishedduring

thetimeIworkasaPhDstudentatGraduateUniversityofScienceandTechnology,Vietna

mAcademy of Science andTechnology.

Hanoi,2024

PhDstudent

Vu Duy Hien

ACKNOWLEDGEMENTS

Scientificresearchisaninterestingjourneywherethethesisisoneofthefirst
resultsthatresearchershavereached.Onthatjourney,Ihavemetmanykindpeoplewh
ohavesupportedformetofinishthisthesis.

Firstofall,IwouldliketothankmygreatsupervisorsProf.Dr.HoTuBaoandAssoc.
Prof.Dr.LuongTheDungwhohaveprovidedvaluableadvicetome.Withouttheirsuppor
tandguidance,Iwouldnotabletocompletemythesis.Ihavelearnedalotofthingsfro
mmysupervisors.

IamthankfultoGraduateUniversityofScienceandTechnology,colleagues
atBankingAcademyofVietnam,friends,andcollaboratorswhoalwaysencour- age me along
my researchjourney.

I also thankthe CAMEL cafe (No.104/1Viet Hung street, Long Bien
dis- trict, Ha Noi)where my publications and thesis had been born in.

Finally, I want to send the most special thank tomy big family, my wife,
and
our childrenwho always have my back.

Hanoi,2024
PhDstudent

Vu Duy Hien

CONTENTS

INTRODUCTION................................................................................1

1 OVERVIEW OF SECURE MULTI-PARTY SUM COMPUTATION 7

1.1 Background of securemulti-partycomputation 7
. . . . . . . . . . ..

1.1.1 Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.1.2 Basic concept . . . . . . . . . . . . . . . . . . . . . . . . .. 10

1.1.3 Definitionofsecurity. . . . . . . . . . . . . . . . . . . . . . 11

1.1.4 Cryptographicpreliminaries.. . . . . . . . . . . . . . . . . 18

1.2 Securemulti-partysumcomputationproblem.. . . . . . . . . . . . 22

1.2.1 Problemformulation.. . . . . . . . . . . . . . . . . . . . . 22

1.2.2 Relatedwork. . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 35

2 PROPOSING EFFICIENT SECUREMULTI-PARTYSUMCOMPUTA-

TIONPROTOCOLS 36

2.1 Analysis of typical secure multi-party sum computation protocols 36
2.1.1 Simple secure multi-party sumcomputationprotocol..........36

2.1.2 Securemulti-partysumcomputationprotocolofUrabeetal...............38

2.1.3 Secure multi-party sum computation protocol of Hao etal.,

2010inanelectronicvotingsystem.......................................40

2.1.4 Privacy-preserving frequency computation protocol ofYang

etal............................................................................44

2.1.5 Furtherdiscussion..............................................................47

2.2 Proposed secure multi-party sumcomputationprotocols..................49

2.2.1 Privacy-preserving frequency computation protocol based
on ellipticcurveElGamalcryptosystem
50

2.2.2 An efficient approach for secure multi-party sum
computationwithout pre-establishingsecure/authenticatedchannels
61

2.2.3 Secure multi-sumcomputation protocol............................78
2.3 Conclusion...............................................................................91

3 DEVELOPING NEW SOLUTIONS BASED ON SECUREMULTI-
PARTYSUMCOMPUTATIONPROTOCOLSFORPRACTICALPROBLEMS
93
3.1 Anefficientsolutionforthesecureelectronicvotingschemewithout
pre-establishingauthenticatedchannel...........................................93

3.1.1 Introduction..................................................................93
3.1.2 Relatedwork...................................................................94
3.1.3 Preliminaries.................................................................96
3.1.4 Asecureend-to-endelectronicvotingscheme...............................97
3.1.5 Securityanalysis..............................................................99
3.1.6 Experimentalevaluation...................................................102
3.2 An efficient and practical solution for privacy-
preservingNaiveBayes classificationinthehorizontaldatasetting
103
3.2.1 Introduction.................................................................104
3.2.2 Relatedwork.................................................................107
3.2.3 Preliminaries...............................................................109
3.2.4 Newprivacy-preservingNaiveBayes classifier for the
hori-zontalpartitiondatasetting
112
3.2.5 Privacyanalysis..............................................................115
3.2.6 Accuracyanalysis...........................................................115
3.2.7 Experimentalevaluation...................................................115
3.3 Conclusion.............................................................................120
CONCLUSION......................................................................................122
BIBLIOGRAPHY..........................................................................124
APPENDICES.............................................................................137

PUBLICATIONL I S T ............................................................... 140

LIST OF ABBREVIATIONS

BoW..................Bag-of-Words
CDH..................ComputationalDiffie-Hellman
DDH..................DecisionalDiffie-Hellman

DD-PKE............Public-keyencryptionwithadouble-decryptionalgorithm
DNA...................Deoxyribonucleicacid
DRE...................Direct-recordingelectronic
DSS....................Digital signaturestandard
E2E....................End-to-end
LWE..................Learn witherror
NSC...................NationaluniversityofSingaporeshorttextmessagescorpus
PPFC.................Privacy-preserving frequencycomputation
PPML................Privacy-preserving machinelearning
PPNBC..............Privacy-preservingNaiveBayesclassification
PSI.....................Private setintersection
RAM..................Random AccessMachines
SMC..................Secure multi-partycomputation
SMS...................Secure multi-partysum
SSC....................Secure sumcomputation
TF-IDF..............Termfrequency –inversedocumentfrequency
UK.....................UnitedKingdom
ZKP...................Zero knowledgeproof

LIST OF TABLES

2.1 ThebriefcomparisonsofthecomputationalcomplexityamongthreetypicalSMSpr
otocols
48

2.2 Thecomputationalcomplexitycomparisonsamongtheproposedpro-
tocolandthetypicalprotocols...........................................................56

2.3 ThecommunicationcostcomparisonsamongthetypicalPPFCprotocols.57
2.4 Thestoreddatavolumeoftheminercomparisonsamongthetypical

PPFCprotocols(inmegabytes)..........................................................62
2.5 Thecomparisonsofeachuser’scomputationalcomplexityamongtheproposed

protocolandthetypicalprotocols.
72
2.6 Theminer’scomputational complexity comparisons among
the pro-posedprotocolandthetypicalprotocols.
72
2.7 The comparisons of eachuser’scommunication cost among
the pro-posedprotocolandthetypicalprotocols.
74
2.8 Thecomparisonsoftheminer’scommunicationcostamongthepro-
posedprotocolandthetypicalprotocols.
74
2.9 The stored data volume of the miner comparisons among
the pro-posedprotocolandthetypicalprotocols(inmegabytes).
78
2.10 Thecomputationalcomplexitycomparisonsamongthenewproposal
andthetypicalsolutions...................................................................86
2.11The communication cost comparison among thenewproposal
and the typicalsolutions.
87
2.12 Therunningtimefortheminertocomputethesumvaluescompar-
isonsamongthecomparedsolutions(inseconds)....................................91

2.13The stored data volume of the miner comparisons among thecom-
paredsolutions(inmegabytes)...........................................................91

3.1 Spam short-messagesdatasetinformation...................................118

3.2 Therunningtimecomparisonsamongthenewproposalandthetypi-
calPPNBCsolutionsontherealdataset(inseconds).........................................119

LIST OF FIGURES

1.1 Thedistributedcomputingmodelinasecuremanner.....................................8
1.2 An example of the authentication method without

knowinguser’spassword
8
1.3 Anexampleofmonitoringuser’spasswords.............................................9
1.4 AnexampleoftheDNApattern-matchingproblem.......................................9
1.5 Thesecureelectronicsealed-bidauctionmodel........................................10
1.6 Therealandidealmodelsindistributedcomputingfield..................................15
1.7 The computational model of the secure multi-party sum
computation problem
22
1.8 Thesingle-candidateendtoenddecentralizede-votingmodel.............................23
1.9 Anexampleoftheprivacy-preservingfrequentitemsetminingproblem23

2.1 Thecomputationalmodelofthesimplesecuremulti-partysumcom-
putationprotocol
37

2.2 TherunningtimeofeachusercomparisonsamongthetypicalPPFC
protocols.............................................................................59

2.3 Thetimefortheminer/theservercomputingthepublickeyscompar-
isonsamongthetypicalPPFCprotocols.......................................................60

2.4 The time for the miner/the server computing the frequency
value com-parisons among the typicalPPFCprotocols.
61

2.5 Therunningtimeofeachusercomparisonsamongtheproposedpro-
tocolandthetypicalprotocols...........................................................75

2.6 Thetimeofthepre-computationphasecomparisonsamongthepro-
posedprotocolandthetypicalprotocols.
76

2.7 The time of the user authentication phase comparisons
among theproposedprotocolandthetypicalprotocols.
77

2.8 The time of the securen-parties sum phase comparisons
among theproposedprotocolandthetypicalprotocols.
78

2.9 Thenumberofprivatekeyscomparisonsamongthecomparedsolutions.88
2.10 The total running time of each user comparisons among the

compared solutions.
89
2.11 Therunningtimefortheminertocomputethepublickeyscompar-
isonsamongthecomparedsolutions.....................................................90

3.1 Thesingle-candidateE2Edecentralizedelectronicvotingmodel.........................96
3.2 The total running time of each voter comparisons between

thenewsolution andHao’sscheme.
103
3.3 Thevotingserver’stotalrunningtimecomparisonsbetweenthenewsolution
andHao’sscheme.
104
3.4 The horizontally distributedcomputingmodel................................111
3.5 Anexampleofdatatransformation......................................................112

1

INTRODUCTION

A. Motivation

Nowadays, the development of information technology and

communication,especiallythebirthofwebapplicationsorinformationsystemsh

ascreatedalarge

amountofdataownedbyorganizationsorindividuals.Thishasspurredthedevel-

opmentofthedistributedcomputingfieldwherethedataownersperformtogether

computationaltasksbasedontheircooperativedata[1,2].Basically,thedistributedcomput

ingfieldhasbroughtalotofsubstantialbenefitstoorganizationsandindivid-uals,

such asreducing significantly costs, understanding comprehensively

customers,and making good business decisions.However,in fact,

because ofprivacypolicy

orbusinesssecrets,participantsofdistributedcomputingsystemsoftenwishtoob-

taincooperativetasks’correctoutputwithoutrevealingtheirinputdata.Forinstance,

somebankscooperatetogethertoimprovemachinelearning-

basedcreditscoringtoolusingtheircustomers’data,buttheyarenotreadytosharetheircu

stomers’dataforanyone.Similarly,althoughtherearesomehospitalswhowanttojoi

ntlydevelopdis-

easediagnosismethodsbasedonalargeuniteddatabase,howevertheydonotwanttopro

videtheirpatients’datatoothers.ThesechallengeshadmotivatedthebirthofSECURE MULTI-

PARTY COMPUTATIONarea (SMC, for short) that has been considered asa

subfield of moderncryptography.

In essence,Secure Multi-party Computationrefers distributed

computingmethods in security concerns [1, 3]. Particularly, in a secure

multi-party computation

model,thereareseveralparties,inwhicheachparticipantownsaprivateinput.These

participantswishtoobtaintheresultofthespecificfunctionfoverallprivateinputs

whileeachpartyrevealsnothingabouthis/herinputbuttheoutputresult.Unliketraditi

onalcryptographyfield,theadversaryofSMCproblemsingeneralandtheSMSproblemi

nparticularcanbeinsidethesystemofparticipants.Theattacksofthead-

2
versarymaybetolearnthehonestparticipants’privateinputortocausetheoutputstobeinco

rrect[1].Asaresult,the”secure”termheremeans:(1)theoutput’scor-

rectness is guaranteed, and(2) each party’s input is privately kept by himself/herself.

Nowadays,SMChasbecomeaninterestingtopicthathasattractedmoreandmor

eattentionfromresearchcommunity.AvarietyofSMCproblemshavebeenfor-

mulatedandtheirsolutionshavebeenproposedintoSMCprotocols,suchassecurecompar

ison protocols[4,5],secure multi-party sum computation protocols [6–8],

and securedotproductprotocols[6,9–

11].Furthermore,suchSMCprotocolshavebeenap-

pliedtovariouspracticalproblems,suchassecureonlineauction[14],securee-voting

systems[12,13],privacy-preserving queries system [15], privacy-

preserving financial data analytic [16], privacy-preserving online

advertising [17], andprivacy-preservingmachine learning/data mining[18–

20].

This thesis has investigated one of the most important and

popularSMCprob-lems[6]thatisthesecuremulti-

partysumcomputationone(SMS,forshort).IntheSMSproblem,itisassumedthatwher

etherearesomeparties,inwhicheachparty

ownsaprivatevalueashis/herinput,andthepartieswishtoobtainthesumofall

inputsbuttheyrevealnothingabouttheirinputsbeyondthesumvalue.S i m i l a r l y to

SMCproblemsingeneral,thebirthofSMSonehasbeenbasedonthesecurityrequirem

entsofspecificdistributedcomputingproblems.Currently,alotofproto-

colshavebeenpropoundedfortheSMSproblem,andtheyhaveawideapplicabilityin

various practical computing tasks, such as privacy-preserving recommendationsys-

tem [21], privacy-preserving multi-party data analytics [22], secure

electronicvotingsystem [12, 13], privacy-preserving association rule

mining [6, 7], privacy-

preservingclassification[23],securedatacollectionforthesmartgrid[24],andse

cureauc-tion [25,26].

ForSMCproblemsingeneral,andSMSoneinparticular,theprotocolsmustbese

cure(mainlyincludingthepreservationoftheprivacyoftheparticipants’local

inputsandthecorrectnessofthehonestparties’outputs[3])enoughtopreventtheadve

rsary’s harmful behaviors. Besides,SMSprotocols should be good

performance(i.e.lowcomputationalcomplexityandcommunicationcost)tobeimple

mentedinreal-life applications. This is perfectly understandable,

because a lot of

practicalSMSproblemsrequiretoperformcomputationaltasksasquicklyaspossible,s
uch

as secure e-voting, secure online auction.SMSprotocols-basedprivacy-

preservationsolutions such as privacy-preserving Apriori algorithm for

mining associationrules,privacy-

preservingNaiveBayesclassifier,andsecuregradientdescentalgorithmhaveto

executeSMSprotocol multiple times to compute necessary mediate values.

More-

over,inmanydistributedcomputingscenarios,participantsusedeviceslimitedinco

mputationalability,storage capacity, and connectivity, e.g. smartphones,

tablets.Thus,itissignificanttodevelopSMSprotocolshavingbothhighsecuritylevelan

dgoodperformance.

B. Researchobjectives

Asmentionedbefore,firstofall,SMSprotocolsneedtobesecure.Todothis,SMSproto
colseither(1)requireeachparticipanttosplithis/herprivatevalueintoa
numberofparts,andhethensharesthemwithallothersusingsecurecommunica-
tionchannelsor(2)usehomomorphiccryptosystemssuchasElGamalencryption
scheme[27]orPailliercryptosystem[28].C o n s i d e r i n g theapproach(1),suchpr
o-tocolsobviouslyhavehighcostofcommunication,andtheyareunsuitableformulti-
partycomputationalmodelswithalargenumberofparticipants.I n contrast,SMSprot
ocolsbasedonthesecondapproach(2)oftenhavepriceycostofcomputation.
Asaresult,itcanbestatedthatthebiggestchallengefordesigningSMSprotocolsishowtocre
ateSMSprotocolshavingbothhighsecuritylevelandgoodperformance.Thus,theresearch
objectivesofthisthesisinclude:

• Designing efficient and secure multi-party sum computation
protocols
thathavethecapabilitytopreservetheprivacyoftheparties’localinputs
andthecorrectnessofthehonestparties’outputs,aswellasgoodperformance.

• DevelopingSMS-based solutions for practical problems
thathavebeencur-
rentlysolvedbyexistingSMSprotocolsbutarenotyetsecureandefficient.

C. Maincontributions
The scientific story of this thesis is narrated as follows: