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Secure Data Outsourcing Outline Motivation Background Research issues Summary Motivation Cost of maintaining/mining large data 4-5 times of the cost of data acquisition DBAs are paid well More and more data service providers Low cost – cloud computing Maintain one database for one user multiple users Examples: Alentus.com Datapipe.com Discountasp.net … Concerns about data security and privacy Untrusted service provider Un-trusted service provider Lazy: incentives to perform less Curious: incentives to acquire information Malicious: Denial of service Incorrect results Possibly compromised Challenges Data confidentiality Data need to be encrypted (?) Utility of protected data? Query utility Mining utility Access pattern privacy Integrity Data integrity Query integrity Correct Complete Fresh Why is it hard for query services? Arbitrary expressivity SQL statements Often, restricted for certain type of query for simplicity (e.g. range query, knn query) Cost Communication Computation (server side vs client side) Why it is hard for mining services? Many data mining models Different utilities to preserve No one-size-for-all solutions Data confidentiality Bucketization method (crypto-index) Order preserving encryption Perturbations Bucketization method Hacigumus (SIGMOD02) Main steps Partition sensitive attributes Order preserving: supports comparison Random: query rewriting becomes hard Build index on the partitions Rewrite queries to target partitions ‘john doe’ 105 Select * from T’ where name=105 Execute queries and return results Prune/post-process results on client Trade off between confidentiality and overhead Larger partition increased privacy increased overheads Order preserving encryption Agrawal2004, Boldyreva2009 The set of data is securely transformed so that the order is preserved but the distribution and domain are changed Benefits: indexing/searching on OPE encrypted data Weakness: once the original distribution is known, OPE is broken Not attribute-wise order preserving Order preserving encryption (OPE, Agrawal et al 2004) is not resilient to distribution-based attacks OPE Original Xi distribution is known Bucket based Estimation Transformed Xi’ distribution Data perturbation Definition 1. randomly change the original data 2. the attacker cannot effectively recover the original data 3. the desired properties are preserved Techniques Single dimension: noise addition Multidimensional Geometric perturbation Random projection RASP random space perturbation Noise addition Y = X+ R X: original data column, R: random noise (distribution published), Y: published data Applications in data mining Reconstructing column distribution Rakesh Agrawal SIGMOD 2000 Applied to privacy-preserving decision tree, naïve bayes classifier Attacks Spectral filtering (Kargupta ICDM 2004) PCA reconstruction (Huang Multiplicative perturbations Geometric data perturbation for outsourced data mining Random Projection RASP perturbation for query services (range query, kNN query). Perturbation-based framework Mining service Geometric data perturbation Y=RX+T+D R: secret rotation matrix (preserve Euclidean distances) T: secret random translation matrix, D: secret random noise matrix Distances are approximately preserved (D) Resilient to most attacks to rotation perturbation Applications Outsourced privacy preserving data mining, applicable for many classification and clustering algorithms Attacks Population based attacks (when covariance matrix is revealed) Random Projection Y=AX+D A: random projection, e.g., entries from N(0,1) Distances are approximately preserved Applications Many classification and clustering algorithms Worse accuracy than geometric perturbation Good for sparse high-dimensional data (text data), i.e., sketch methods (A is randomly generated for EACH record) Attacks Possibly more resilient than other two perturbation methods But utility (distance) is not well preserved RASP perturbation k-dimensional numeric data, n records, represented as a k x n matrix, x: a record (1) Extend x to k+2 dimensions - (K+1) th dimension is always 1 – homogeneous dimension (K+2) th dimension v is a real random number drawn from (2) Encryption - A is a (k+2)x(k+2) invertible real value matrix, with at least two non-zero values for each row and the last column of A has all non-zero values - A is shared by all records Properties Not an OPE Preserves convexity of the dataset Convex dataset in Rk another convex dataset in Rk+2. Good for range query Each range query in Rk hyperplane based query range query in Rk+2 . RASP properties Convexity preserving Queried range (hypercube) is convex RASP transforms the range to another convex (polyhedron) half space: wTx<=a wTx=a The intersection of convex sets is also convex. illustration of convexity preserving Original space Encrypted space Secure query transformation A naïve solution Based on the convexity preserving property Problems: (1) A-1 can be probed (2) is . . If a is known, the whole dimension i is breached. Secure query transformation Enhanced solution Xk+2 is always positive (Xi-a) 0 (Xi-a)Xk+2 0 Correspondingly, in the encrypted space yTy 0, Problems addressed: (1) A-1 cannot be derived from (2) (Xi-a)Xk+2 0 contains the random component Xk+2 that protects the condition (Xi-a) 0 Efficient two-stage query processing illustrated Stage2: Filter out the junk records Stage1: Querying this bounding box Original space Transformed space A multidimensional tree index is been built on the encrypted data (in the transformed space) in the server. Stage 1: The client calculates the large bounding box; The server uses the index to find the results. Stage 2: filter the initial results with the conditions yTiy 0 for 1…2k Note: the two-stage strategy works, if the output of stage 1 is significantly smaller than the original database and can be fit into the memory. Otherwise, use linear scan with stage 2 filtering. RASP-based data mining Preserving range query linear classifier Use the boosting framework to get strong classifiers (PerturBoost, in ICDM 2013) Access pattern privacy On database queries Problem is the same as PIR Attackers may use the access pattern to breach data confidentiality Each of previous approaches should handle this problem! PIR is impractical Solutions based on private Information retrieval (PIR) PIR is still impractical For Bucktization approach Based on the architecture of Hacigumus (SIGMOD02) Hore VLDB04 For range query Privacy concern: reveal the distribution of value in each bucket “Diffusion”: split buckets and combine parts of different buckets Trade off: now the server needs to return more noisy results larger size For OPE Use queries to find out the distributions, then break the encryption For RASP Secure query transformation Attacks to transformed queries Oblivious RAM Access pattern: read/write data items Setting: Client has a small secure memory Server has large insecure storage, semihonest Data items are encrypted Client cannot hide the accessed locations An active area Existing Approaches Dummy Block Real Block Real Block Dummy Block Real Block Dummy Block Dummy Block Real Block Inside a level Some real blocks Useful data Some dummy blocks Random data Randomly permuted Only the client knows the permutation Existing Approaches Reading Read a block from each level One real block. Remaining are dummy blocks dummy real dummy dummy dummy dummy Server Client Existing Approaches Server (before) Client Server (after) Writing Shuffle consecutively filled levels. Write into next unfilled level. Clear the source levels shuffle blocks Continuous Shuffling … To write: The Problem with Existing Approaches Integrity guarantee Merkle hash tree H(H(x1)+H(x2)) , + is string concatenation Can be stored with tree like structure : index, xml Hash chains Query correctness with merkle by Devanbu et. al. Using merkle tree Example: 5<=q<=10 LUB(q) = 4 GLB(q) = 11 Operations: Selections, projections, equijoins, set ops Issues Works only on data with verification objects Query expressiveness Expensive Related work Pang et. al (ICDE04, SIGMOD05), using ElGamal function Sion VLDB05: challenge token F.Li SIGMOD06: freshness Secure keyword search Simple information retrieval For a keyword, find the documents containing the keyword What if the documents are encrypted word by word and if the keyword is also encrypted Secure keyword search Song 2000 •Seed is random, different for each Wi •Key idea: Li and Ri are selfverifiable •Advantage of XOR How to set K? Setting of ki Ki = Fk’(Wi), k’ is secret User publishes W and k = Fk’(W) Server checks CiW whether <Li, Fk(Li)> == CiW It reveals nothing if Ci is not the ciphertext for W. And Li is random for different Wi – server cannot find any information from Li. Hidden search In previous schemes, W is revealed Weakness: each search will have to release k for W Easy to collect information Solution: encrypt Wi with an private key, then xor with <Li, Fk(Li)> Recent developments Reza 2006 “Searchable symmetric encryption: improved definitions and efficient constructions” Completely solved this problem, with a solution indistinguishability under chosen ciphertext attack (IND-CCA) Trusted hardware Possible benefits Discussion Data confidentiality/access pattern Restrict cryptographic definition (keyword search) or Relaxed definition (perturbation, bucketization, OPE, etc.) It is very difficult to formulate and prove the security of non-traditional approaches Do we need to reformulate the security model? and how?
