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Secure and Privacy-Preserving Database Services in the Cloud Divy Agrawal, Amr El Abbadi, Shiyuan Wang University of California, Santa Barbara {agrawal,

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Presentation on theme: "Secure and Privacy-Preserving Database Services in the Cloud Divy Agrawal, Amr El Abbadi, Shiyuan Wang University of California, Santa Barbara {agrawal,"— Presentation transcript:

1 Secure and Privacy-Preserving Database Services in the Cloud Divy Agrawal, Amr El Abbadi, Shiyuan Wang University of California, Santa Barbara {agrawal, amr, sywang} ICDE2013 Tutorial

2 Cloud Computing Successful paradigm for computing and storage Features – Pay per use – No up-front cost for deployment – Scalability – Elasticity Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) 4/11/2013ICDE 2013 Tutorial2

3 Adopting the Cloud Emails Collaboration Administrative apps Conferencing software Education 4/11/2013ICDE 2013 Tutorial3 Early adopters are mainly low risk apps with less sensitive data Sensitive Data

4 Cloud – A Tempting Attack Target Why the cloud? – Ubiquitous access to consolidated data. – Shared infrastructure economies of scale – A lot of small and medium businesses Why attack? – Target one service provider, attack multiple companies – Financial gain from trading sensitive information 4/11/2013ICDE 2013 Tutorial4

5 Cloud Provides Novel Attack Opportunities Co-residence attack [Ristenpart et al. CCS09] – Adversary: non-provider-affiliated malicious parties – Map and identify location of target VM – Place attacker VM co-resident with target VM – Cross-VM side-channel attacks (due to sharing of physical resources): eg, number of visitors to a page, or keystroke attacks for password retrieval. Signature wrapping attack [Somorovsky et al. CCSW11] – Control Interface compromise by capturing a SOAP msg. – Manipulate SOAP message with arbitrary XML fragments – Use XML signature vulnerability to pass authentication – Take control of a victims account 4/11/2013ICDE 2013 Tutorial5

6 Amazons Best Practices for Cloud Security and Privacy Concerns – Co-residence attacks – Side channel attacks – Network based attacks – Unauthorized accesses – Insider attacks – Privacy violation – Future vulnerabilities? 4/11/2013ICDE 2013 Tutorial6 Defenses [AWS security] – dedicated instances, virtual private cloud, isolated network and traffic – Firewall and access control – Identity and access management, multi-factor authentication – accesses checked and audited – Rely on clients for access control – Recommend using data encryption and encrypted file system Best effort defense is not sufficient

7 A Barrier to Conquer Security and privacy – a barrier to cloud adoption Data (sensitive data) – a key concern We need to solve data security and privacy problems in the cloud 4/11/2013ICDE 2013 Tutorial7

8 Outline Database Security and Privacy: General Practice in the DB Community Data Security and Privacy in the Cloud Data Confidentiality Access Privacy Open Research Challenges 4/11/2013ICDE 2013 Tutorial8

9 Access Control [Bertino et al. TDSC05] Problem Statement: authorizing data access scopes (relations, attributes, tuples) to users of DBMS Discretionary access control – Authorization administration policies, ie, granting and revoking authorization (centralized, ownership, etc) – Content-based using views and rewriting for fine-grained access control – Role-based access control: a function with a set of actions, consisting of users members Mandatory access control: – Object and subject classification (eg, top secret, secret, unclassified, etc). 4/11/2013ICDE 2013 Tutorial9

10 Data Anonymization Problem: protecting Personally Identifiable Information (PII) and their sensitive attributes 4/11/2013ICDE 2013 Tutorial10 Quasi-identifierSensitive DOBGenderZipcodeDisease 1/21/76Male53715Heart Disease 4/13/86Female53715Hepatitis 2/28/76Male53703Brochitis 1/21/76Male53703Broken Arm 4/13/86Female53706Flu 2/28/76Female53706Hang Nail Quasi-identifiers need to be generalized or suppressed Quasi-identifiers are sets of attributes that can be linked with external data to uniquely identify an individual

11 Equivalence class share same QI Solution: k-Anonymity [Samarati et al. TR98] Quasi-identifiers indistinguishable among k individuals Implemented by building generalization hierarchy or partitioning multi-dimensional data space 4/11/2013ICDE 2013 Tutorial11

12 Enhanced Solution: l-Diversity [Machanavajjhala et al. ICDE06] At least l values for sensitive attributes in each equivalence class 4/11/2013ICDE 2013 Tutorial12 ZipcodeAgeSalaryDisease 476**2*20KGastric Ulcer 476**2*25KGastritis 476**2*30KStomach Cancer 4790*4050KGastritis 4790*40100KFlu 4790*4070KBronchitis 476**3*60KBronchitis 476**3*80KPneumonia 476**3*90KStomach Cancer A 3-diverse patient table

13 Enhanced Solution: t-Closeness [Li et al. ICDE07] Distance between overall distribution of sensitive attribute values and distribution of sensitive attribute values in an equivalence class bounded by t 4/11/2013ICDE 2013 Tutorial13

14 Privacy-Preserving Data Mining Problems: hide sensitive rules or private individual data in data mining [Verykios et al. SIGMOD04] – 1. sanitize sensitive item sets or sensitive rules – 2. build data mining model without access to precise data, e.g. privacy-preserving classification, clustering – 3. private parties compute together on their private inputs, e.g. distributed association rule mining, collaborative filtering Solutions – 1. Data perturbation, blocking rule confusion – 2. Data perturbation Distribution reconstruction [Agrawal et al. SIGMOD00, PODS01] – 3. Secure Multi-party Computation (SMC) [Clifton et al. KDD02] 4/11/2013ICDE 2013 Tutorial14

15 Differential Privacy for Statistical Data [Dwork ICALP06] 4/11/2013ICDE 2013 Tutorial15

16 A randomized function K gives ε-Differential Privacy IFF for all datasets D 1 and D 2 differing on at most one element, and all S Range (K) Strong privacy guarantees while querying a database 16 Query A PERTURBATION P(A) Query A PERTURBATION P(A) Indistiguishable! Thanks to Ben Zhao for this slide Differential Privacy for Statistical Data [Dwork ICALP06]

17 Access Control & Privacy [Chaudhuri et al. CIDR11] 4/11/2013ICDE 2013 Tutorial17 Hybrid System combining authorization predicates and noisy views

18 Secure Devices for Privacy [Anciaux et al. SIGMOD07] Problem: protecting private data during queries involving both private (hidden) and public (visible) data Solution: carry private data in a secure USB key, ensure private data never leaves the USB key, and only public data flows to the key Query optimization for small RAM USB key 4/11/2013ICDE 2013 Tutorial18

19 Outline Database Security and Privacy Data Security and Privacy in the Cloud Data Confidentiality Access Privacy Open Research Challenges 4/11/2013ICDE 2013 Tutorial19


21 Problems Amplified by the Cloud 4/11/2013ICDE 2013 Tutorial21 Data confidentiality – Attacks Unauthorized accesses, side channel attacks – Solutions Encryption, querying encrypted data Trusted computing User Cloud Servers Data Query Answer Access privacy – Attacks Inferences on access patterns or query results – Solutions Private information retrieval Query obfuscation

22 Data Services in the Cloud 4/11/2013ICDE 2013 Tutorial22 DB Queries Functionality Performance Adversaries: curious but not malicious cloud / insiders 3 rd party attackers Actions: obtain / infer data and queries

23 Challenges: Conflicting Goals 4/11/2013ICDE 2013 Tutorial23 Existing Services Functionality Performance Confidentiality / Privacy High Low High Many Crypto Systems/Protocols Ideal State

24 Outline Database Security and Privacy Data Security and Privacy in the Cloud Data Confidentiality Access Privacy Open Research Challenges 4/11/2013ICDE 2013 Tutorial24

25 Data Confidentiality 1. Encryption – Homomorphic encryption – Partition Index – Order-preserving encryption – Encrypted Index 2. Leveraging Trust – Distribution – Trusted computing 4/11/2013ICDE 2013 Tutorial25

26 Database as a Service [Hacigümüs et al. ICDE02] Protects data from steeling but plaintext data can still be seen on the server Write – encrypt before storing – insert into lineitem (discount) values (encrypt(10,key)) Read – decrypt before access – select decrypt(discount,key) from lineitem where custid = 300 Encryption alternatives – Software level v.s. Hardware level (cryptographic coprocessor) encryption – Granularity: field, row, page 4/11/2013ICDE 2013 Tutorial26

27 Keyword Search on Encrypted Texts [Song et al. S&P00] Directly search on encrypted data without decryption on server side Encrypt word by word. For word W i – Block_ciphertext X i = E k (W i ), Word key k i = f k (X i ), Pseudorandom sequence T i = – Searchable_ciphertext C i = X i T i Search for a word W – Block_ciphertext X = E k (W), Word key k i = f k (X) – Check ciphertexts one by one to see if C X = (X i T i ) X is of the form for some random value s 4/11/2013ICDE 2013 Tutorial27

28 Homomorphic Encryption 4/11/2013ICDE 2013 Tutorial28

29 Homomorphic Encryption 4/11/2013ICDE 2013 Tutorial29 OperationX86-64 Intel Core 2 @ 2.1 GHz SH_Keygen250 ms SH_Enc24 ms SH_Add1 ms SH_Mul41 ms SH_Dec (2-element ciphertext)15 ms SH_Dec (3-element ciphertext)26 ms From Kristen Lauters Slides @ MSR Faculty Summit 2011 1 million data Aggregation: 16 minutes Range query: 11 hours Too expensive to be practical


31 Partition and Identification Index [Hacigümüs et al. SIGMOD02] E(tuple): encrypted-tuple, {attribute-index} Attribute-index: attribute value partition ids 4/11/2013ICDE 2013 Tutorial31 2 0 200 400 600 8001000 7514

32 Partition and Identification Index Client knows a map function, Map(val) = id of the partition containing val 4/11/2013ICDE 2013 Tutorial32 2 0 200 400 600 8001000 7514 1 0 200 400 600 8001000 2457 Random mapping Order-preserving mapping

33 Mapping Predicate Conditions Map(< val) : ids of the partitions that could contain values < val E.g. Map(eid < 280) = {2, 7} for random mapping Map(> val) : ids of the partitions that could contain values > val Map(A i = A j ): pairs of ids of the partitions that could have equal A i and A j values Decryption and processing on the client 4/11/2013ICDE 2013 Tutorial33

34 Mapping Predicate Conditions 4/11/2013ICDE 2013 Tutorial34 emp.did = mrg.did

35 Optimal Partition for Range Queries [Hore et al. VLDB04] Optimal for privacy-performance tradeoff Performance: minimize number of false positives over all range queries in a given query distribution – False positives caused by server returning a superset of answers Privacy: maximize variance, entropy of value distribution in a partition – High variance – increase adversaries error in inferring sensitive attribute values – High entropy – reduce adversaries ability to identify encrypted tuples satisfying a plaintext query 4/11/2013ICDE 2013 Tutorial35

36 Partition / Bucketization Review Pros – Efficient computation on the server Cons – Data update is hard (may need re-distribution) – Filtering super answer set could be time consuming depending on the partitions sizes – Might reveal value distribution from relative partitions changes during dynamic data updates 4/11/2013ICDE 2013 Tutorial36

37 Can Ciphertext Be Queried Directly Encryption with special properties that allow predicate evaluation on ciphertexts Order-preserving partition mapping order- preserving encryption 4/11/2013ICDE 2013 Tutorial37

38 Order Preserving Encryption [Agrawal et al. SIGMOD04] 4/11/2013ICDE 2013 Tutorial38

39 Achieving Order Preserving Encryption 4/11/2013ICDE 2013 Tutorial39

40 Order-Preserving Review Pros – Return exact answers instead of super sets – Can leverage existing DB index Cons – Hard to perform analysis and aggregation – Some tuples could be easily identified if approach is applied to multiple attributes 4/11/2013ICDE 2013 Tutorial40

41 CryptDB [Popa et al. SOSP11] Supports a wide range of SQL queries over encrypted data Server fully evaluates queries on encrypted data, and client does not perform query processing SQL-aware encryption – leverage provable practical techniques for different SQL operators over encrypted data Adjustable query-based encryption – Dynamically adjust the encryption level of data items according to users queries Onion of encryptions – From weaker forms of encryption that allow certain computation to stronger forms of encryption that reveal no information 4/11/2013ICDE 2013 Tutorial41

42 SQL-Aware Onion Encryption 4/11/2013ICDE 2013 Tutorial42 RND: no functionality DET: equality selection SEARCH: word selection (only for text fields) Any value JOIN: equality join RND: no functionality OPE: comparison Any value OPE-JOIN: inequality join int value HOM: sum

43 CryptDB System 4/11/2013ICDE 2013 Tutorial43 For performing cryptographic operations For sending certain onion layer key

44 CryptDB Review Pros – Support a wide range of SQL queries Cons – Confidentiality level degrades to the weakest encryption in the long term 4/11/2013ICDE 2013 Tutorial44


46 Encrypted Index for Outsourced Data Build a normal B+-tree index on key values Encrypt B+-tree nodes Store (and disperse) encrypted index in the cloud [Damiani et al. CCS03, Wang et al. SDM11] A query with predicates on keys is processed by locating desired key values on encrypted index. Traversal on index relies on the client to retrieve and decrypt index nodes. 4/11/2013ICDE 2013 Tutorial46

47 4/11/2013ICDE 2013 Tutorial47 A2A1 D: Data Tuples t1t2....,tNt1t2....,tN …………………………………… A1 …………………………………… Ad …………………………………… … …………………………………… A2 I: B+-tree Index … … … … … … …… n1n1 n2n2 … … … ID n1n1 n2n2 … … … IE E(n 2 )E(n 1 ) … … … TD tc 1 tc 2 … … … TE E(tc 2 )E(tc 1 ) SiSi S 1 S n Cloud Servers Salted IDA

48 Practical Secure Query Processing 4/11/2013ICDE 2013 Tutorial48 Client Proxy SiSi S 1 S n Cloud Servers … Index I … … root … … … IE E(n 2 )E(n 1 ) … … … TE E(tc 2 )E(tc 1 ) IE col1 … … n1n1 1 2 ……………… IE :1 E(n 1 ) ……………… IE :1 E(n 1 ) TE col2 ……………… TE :2 E(tc 2 ) ……………… TE :2 E(tc 2 ) Cache partial index nodes on client to improve efficiency

49 Encrypted Index Review Pros – Can be directly deployed on existing cloud settings – Provide stronger confidentiality than partition, order- preserving encryption without losing query efficiency Cons – The Clouds computational ability is under utilized – Queries directly supported are limited to queries on indexed key attributes 4/11/2013ICDE 2013 Tutorial49

50 Data Confidentiality 1. Encryption – Homomorphic encryption – Partition Index – Order-preserving encryption – Encrypted Index 2. Leveraging Trust 4/11/2013ICDE 2013 Tutorial50

51 Distribution instead of Encryption Under non-communicating servers assumption [Aggarwal et al. CIDR05] 4/11/2013ICDE 2013 Tutorial51 Server 1Server 2 Sensitive attributes E(telephone), E(email) Sensitive association name, salary name salary name, E(salary) Query Q1 Q2 Result(Q1) join Result(Q2)

52 Distribution Review Pros – Reduce encryption and decryption overhead Cons – Non-communicating servers assumption is strong* – Data distribution policy is usually not up to a client, but decided by cloud server providers – * [Emekci et al. ICDE06, Agrawal et al. SRDS88, Ciriani et al. ESORICS09] 4/11/2013ICDE 2013 Tutorial52

53 Tamper Resistant Trusted Hardware 4/11/2013ICDE 2013 Tutorial53

54 Computation Cost Consideration 4/11/2013ICDE 2013 Tutorial54

55 Trusteddb [Bajaj et al. SIGMOD11] 4/11/2013ICDE 2013 Tutorial55

56 Trusted Computing Review Pros – Support almost all existing DBMS functionalities Cons – Computing and memory resources are limited Cipherbase [Arasu et al. CIDR13]: better optimization based on trusted hardware – Requires secret key handover from user to trusted hardware 4/11/2013ICDE 2013 Tutorial56

57 Outline Database Security and Privacy Data Security and Privacy in the Cloud Data Confidentiality Access Privacy Open Research Challenges 4/11/2013ICDE 2013 Tutorial57

58 Access Privacy 1. Private Information Retrieval (PIR) 2. Oblivious RAM 3. Relaxing Privacy 4/11/2013ICDE 2013 Tutorial58

59 Private Information Retrieval [Chor et al. JACM98] Multi-servers information theoretic PIR – Implemented based on XOR, polynomial interpolation – Achieves 2-server communication complexity O(n 1/3 ) – Tolerate collusions of up to t < k servers Single-server PIR – Require only computational indistinguishability 4/11/2013ICDE 2013 Tutorial59 X= X1X1 X2X2 ……………… XnXn database Server Client q=give me ith record encrypted(q) encrypted-result=f(X, encrypted(q))XiXi

60 cPIR Theoretical Background Quadratic Residue (QR) x is a quadratic residue (QR) mod N if – E.g. N=35, 11 is QR (9 2 =11 mod 35) – 3 is QNR (no y exists such that y 2 =3 mod 35) – Essential properties: QR ×QR = QR QR ×QNR = QNR Let N =p 1 ×p 2, p 1 and p 2 are large primes of m/2 bits. Quadratic Residuosity Assumption (QRA) – Determining if a number is a QR or a QNR is computationally hard if p 1 and p 2 are not given. 4/11/2013ICDE 2013 Tutorial60

61 Single Database cPIR [Kushilevitz et al. FOCS97] 4/11/2013ICDE 2013 Tutorial61 Adapted from Tans presentation 0101 1101 0101 0111 e g Get M 2,3 N=35 QNR={3,12,13,17,27,33} QR={1,4,9,11,16,29} 4 16 17 11 QNR z4z3z2z1z4z3z2z1 z 2 =QNR => X 10 =1 z 2 =QR => X 10 =0 M 2,3 27 3 2717 Computation cost: O(n) Client Server z:

62 4/11/2013ICDE 2013 Tutorial62 Practicality of PIR [Sion et al. NDSS07, Olumofin et al. FC11] cPIR is more than one order of magnitude slower than trivial data transfer. Multi-server PIR is more practical, but it requires servers cannot collude.

63 PIR Could Be More Practical [Olumofin et al. FC11] Multi-server information-theoretic PIR Single-server lattice-based PIR – Unlike previous cPIR which are based on number theory – Can achieve one order of magnitude speedup by using GPU – Cons: security not well understood as number theory based cPIR 4/11/2013ICDE 2013 Tutorial63

64 Access Privacy 1. Private Information Retrieval (PIR) 2. Oblivious RAM 3. Relaxing Privacy 4/11/2013ICDE 2013 Tutorial64

65 Oblivious RAM Based PIR [Goldreich & Ostrovsky JACM 96 Williams et al. NDSS08] A step towards making PIR practical Oblivious RAM : achieve oblivious access in server memory Organize data in pyramid like levels of buckets Ensure each access touches a bucket at every leve l 4/11/2013ICDE 2013 Tutorial65

66 4/11/2013ICDE 2013 Tutorial66

67 Oblivious RAM Based PIR 4/11/2013ICDE 2013 Tutorial67 Computation cost: O(log 2 n) Needs some client storage during oblivious re-ordering of encrypted data

68 Oblivious RAM Review 4/11/2013ICDE 2013 Tutorial68

69 Access Privacy 1. Private Information Retrieval (PIR) 2. Oblivious RAM 3. Relaxing Privacy 4/11/2013ICDE 2013 Tutorial69

70 Bounding-Box PIR [Wang et al. DBSEC10] 4/11/2013ICDE 2013 Tutorial70 0101 1101 0101 0111 e g Get M 2,3 N=35 QNR={3,12,13,17,27,33} QR={1,4,9,11,16,29} z 2 =QNR => M 2,3 =1 M 2,3 1727 16 17 QNR y:y: z:z: Bounding Box Client Server

71 Hybrid Approach with Homomorphic Encryption [Wang et al. DAPD13] 4/11/2013ICDE 2013 Tutorial71 Client Server 0.1. bucket summary S 0.2. public key K pub 1. query vector Q 2. answer vector V 3. decrypt V & filter R pub.B: [0,100) S1:S1: 0 205060708595100 BK 1 BK 2 BK 3 BK 4 BK 5 BK 6 BK 7 S1S1 K pub Q: [45, 65) Q: (E(0), E(1), E(1), E(1), E(0), E(0), E(0)) V: (E(0) VBK1, E(1) VBK2, E(1) VBK3, E(1) VBK4, E(0) VBK5, E(0) VBK6, E(0) VBK7 ) D(V[2]) = D(E(1) VBK2 ) = D(E(1))*VBK 2 = VBK 2 D(V[3]) = D(E(1) VBK3 ) = D(E(1))*VBK 3 = VBK 3 D(V[4]) = D(E(1) VBK4 ) = D(E(1))*VBK 4 = VBK 4

72 Hybrid Approach with Homomorphic Encryption [Wang et al. DAPD13] 4/11/2013ICDE 2013 Tutorial72 Client selects subset of buckets for server to work on – Private query buckets – Relevant frequently co- accessed sets of buckets of other users Reasons for using frequent bucket sets – Hide in crowd – Less identifiable Server BHE Client query history Client query history private distributed frequent pattern mining [TKDE04] FBS Client Server HHE Q: (0, E(1), E(1), E(1), 0, E(0), E(0))

73 Access Pattern Privacy on Encrypted Index [Vimercati et al. ICDCS11] Not using any cryptographic protocols Cover searches – Fake searches Cached searches – Cache index nodes Index shuffling – Exchange contents between index nodes – Counteract node-data association attacks 4/11/2013ICDE 2013 Tutorial73

74 Index Shuffling 4/11/2013ICDE 2013 Tutorial74

75 Relaxing Privacy Review Pros – More computationally efficient than PIR Cons – (Incomplete) privacy tricky to define and quantify 4/11/2013ICDE 2013 Tutorial75

76 Outline Database Security and Privacy Data Security and Privacy in the Cloud Data Confidentiality Access Privacy Open Research Challenges 4/11/2013ICDE 2013 Tutorial76

77 Open Research Problems Homomorphic encryption for processing range/join database queries on encrypted data Improve performance of querying encrypted data for use in practical OLTP applications – Pre-computation – Parallel calculation End to end security in the cloud – Need information flow control and auditing in addition to cryptography or trusted computing based approaches 4/11/2013ICDE 2013 Tutorial77

78 Concluding Remarks Cloud security and privacy is not a completely new problem. Some issues are amplified by the cloud. Protecting data confidentiality and access privacy Maintaining practical functionality and performance while achieving security and privacy 4/11/2013ICDE 2013 Tutorial78

79 References [Bertino et al. TDSC05] E. Bertino et al. Database security-concepts, approaches, and challenges. In IEEE TDSC, 2(1), 2005. [Samarati et al. TR98] P. Samarati et al. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. TR 1998. [Machanavajjhala et al. ICDE06] A. Machanavajjhala et al. l-diversity: privacy beyond k-anonymity. In ICDE 2006. [Li et al. ICDE07] N. Li et al. t-closeness: privacy beyond k-anonymity and l- diversity. In ICDE 2007. [Dwork ICALP06] C. Dwork. Differential privacy. In ICALP(2) 2006. [Verykios et al. SIGMOD04] V. S. Verykios et al. State-of-the-art in privacy preserving data mining. In SIGMOD 2004. [Agrawal et al. SIGMOD00] R. Agrawal et al. Privacy-preserving data mining. In SIGMOD 2000. [Clifton et al. KDD02] C. Clifton et al. Tools for privacy preserving distributed data mining. In KDD 2002. [Anciaux et al. SIGMOD07] N. Anciaux et al. GhostDB: querying visible and hidden data without leaks. In SIGMOD 2007. 4/11/2013ICDE 2013 Tutorial79

80 References [Chaudhuri et al. CIDR11] S. Chaudhuri et al. Database access control & privacy: is there a common ground? In CIDR 2011. [Ristenpart et al. CCS09] T. Ristenpart et al. Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds. In CCS 2009. [Somorovsky et al. CCSW11] J. Somorovsky et al. All your clouds are belong to us: security analysis of cloud management interfaces. In CCSW 2011. [Hacigümüs et al. ICDE02] H. Hacigümüs et al. Providing database as a service. In ICDE 2002. [Song et al. S&P00] D. Song et al. Practical techniques for searches on encrypted data. In S&P 2000. [Hacigümüs et al. SIGMOD02] H. Hacigümüs et al. Executing SQL over encrypted data in the database service provider mode. In SIGMOD 2002. [Hore et al. VLDB04] B. Hore et al. A privacy-preserving index for range queries. In VLDB 2004. [Agrawal et al. SIGMOD04] R. Agrawal et al. Order preserving encryption for numeric data. In SIGMOD 2004. 4/11/2013ICDE 2013 Tutorial80

81 References [Popa et al. SOSP11] R. A. Popa et al. Cryptdb: protecting confidentiality with encrypted query processing. In SOSP 2011. [Damiani et al. CCS03] E. Damiani et al. Balancing confidentiality and efficiency in untrusted relational DBMSs. In CCS 2003. [Wang et al. SDM11] S. Wang et al. A comprehensive framework for secure query processing on relational data in the cloud. In SDM 2011. [Aggarwal et al. CIDR05] G. Aggarwal et al. Two can keep a secret: a distributed architecture for secure database services. In CIDR 2005. [Emekci et al. ICDE06] F. Emekci et al. Privacy preserving query processing using third parties. In ICDE 2006. [Agrawal et al. SRDS88] D. Agrawal et al. Quorum consensus algorithms for secure and reliable data. In SRDS 1988. [Bajaj et al. SIGMOD11] S. Bajaj et al. Trusteddb: a trusted hardware based database with privacy and data confidentiality. In SIGMOD 2011. [Song et al. IEEE12] D. Song et al. Cloud data protection for the masses. In IEEE Computer, 45(1), 2012. [Chor et al. JACM98] B. Chor et al. Private information retrieval. In J. ACM, 45(6), 1998. 4/11/2013ICDE 2013 Tutorial81

82 References [Kushilevitz et al. FOCS97] E. Kushilevitz et al. Replication is not needed: single database, computationally private information retrieval. In FOCS 1997. [Sion et al. NDSS07] R. Sion et al. On the computational practicality of private information retrieval. In NDSS 2007. [Olumofin et al. FC11] F. G. Olumofin et al. Revisiting the computational practicality of private information retrieval. In FC 2011. [Williams et al. NDSS08] P. Williams et al. Usable private information retrieval. In NDSS 2008. [Wang et al. DBSEC10] S. Wang et al. Generalizing PIR for practical private retrieval of public data. In DBSec 2010. [Wang et al. DAPD13] S. Wang et al. Towards practical private processing of database queries over public data. In DAPD 2013. [Vimercati et al. ICDCS11] S. D. C. Vimercati et al. Efficient and private access to outsourced data. In ICDCS 2011. 4/11/2013ICDE 2013 Tutorial82

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