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COMPUTATION: THE CASE
OF OUTSOURCED
PRIVACY-PRESERVING
SIFT
Zhan Qin , Jingbo Yan, Kui Ren, Chang Wen Chen
State Universit...
iPhoto
Growth of Images
 Tremendous growth in various image data.
 Millions of images are captured and uploaded from loc...
Mining the Image Data
 Valuable information could be mined.
 Important role of Image Data Mining
 Content Based Image R...
Outsourcing them to Cloud
 Enormous workload on image processing
tasks.
 How about outsourcing them to cloud?
 Cloud: F...
The Problem is the Privacy
 Privacy leakage
 Outsourced image reveals private info[1].
 Various users’ requirements
 S...
Popular Image Processing Algorithm and the
privacy
 The state of the art focuses on protecting
image content[2].
 Pixel ...
SIFT Algorithm
 SIFT is an useful and popular algorithm to
detect content features to better enable further
image mining ...
Recall Lowe’s SIFT
 Two main stages
 Scale-space Extrema Detection
 Descriptor Generation
D(x, y,sij )=[G(x, y,kis)-G(x...
Existing Privacy-preserving SIFT
Algorithm
 Possible solution
 Homomorphic Encryption (HE) [4]
 Encryption schemes that...
Limitation of HE-based
solutions
 Limitations of existing HE-based solutions
 Functionality
 Complicated computation li...
Key Ideas
 Balance the tradeoff between utility and privacy
 Reduce complexity.
 Divide the cloud into multiple indepen...
SecSIFT: A Secure SIFT feature detection system
based on Cloud
 We propose a privacy-preserving solution to cloud-based
c...
SecSIFT: Framework
 We divide the original SIFT algorithm into three
stages.
 Three entities: Client, Generators, and Co...
SecSIFT: Image encryption on
Client
 Client
 Encryption system
SecSIFT: Scale-space Cube
Generation
 Generator
 Scale-space Generation
 Cube Encryption
 Cube Permutation: Privacy
 ...
SecSIFT: Keypoint Discovering
 Comparer
 Partially recover the encrypted cubes.
 Return extremes’ id with dummy ids.
OP...
SecSIFT: Descriptor Generation
 Generator
 We utilize four vectors in fixed directions to
approximate the original sift ...
SecSIFT: Experimental
Evaluation
 Utility
 Precision of SecSIFT descriptors
 Location of interesting points.
 Image ma...
SecSIFT: Precision
 Euclidean distance between the corresponding
keypoints.
SecSIFT: Precision
 Error rate of image matching
SecSIFT: Efficiency
 Computation time
SecSIFT
HE-SIFT
SecSIFT: Efficiency
 Workload Distribution
SecSIFT: Privacy
 Confidentiality of pixel values & descriptors.
 One time pad.
 Order preserving encryption.
 Delocal...
Conclusion
 SecSIFT: a novel approach that integrates
SMC and OPE to enable secure image
computation outsourcing with pra...
Thank You
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Towards Efficient Privacy-preserving Image Feature Extraction in Cloud Computing

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As the image data produced by individuals and enterprises is rapidly increasing, Scalar Invariant Feature Transform (SIFT), as a local feature detection algorithm, has been heavily employed in various areas, including object recognition, robotic mapping, etc. In this context, there is a growing need to outsource such image computation with high complexity to cloud for its economic computing resources and on-demand ubiquitous access. However, how to protect the private image data while enabling image computation becomes a major concern. To address this fundamental challenge, we study the privacy requirements in outsourcing SIFT computation and propose SecSIFT, a high performance privacy-preserving SIFT feature detection system. In previous private image computation works, one common approach is to encrypt the private image in a public key based homomorphic scheme that enables the original processing algorithms designed for plaintext domain to be performed over ciphertext domain. In contrast to these works, our system is not restricted by the efficiency limitations of homomorphic encryption scheme. The proposed system distributes the computation procedures of SIFT to a set of independent, co-operative cloud servers, and keeps the outsourced computation procedures as simple as possible to avoid utilizing homomorphic encryption scheme. Thus, it enables implementation with practical computation and communication complexity. Extensive experimental results demonstrate that SecSIFT performs comparably to original SIFT on image benchmarks while capable of preserving the privacy in an efficient way.

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Towards Efficient Privacy-preserving Image Feature Extraction in Cloud Computing

  1. 1. COMPUTATION: THE CASE OF OUTSOURCED PRIVACY-PRESERVING SIFT Zhan Qin , Jingbo Yan, Kui Ren, Chang Wen Chen State University of New York at Buffalo Cong Wang City University of HongKong
  2. 2. iPhoto Growth of Images  Tremendous growth in various image data.  Millions of images are captured and uploaded from local devices to internet every day.  E.g. , , , etc.
  3. 3. Mining the Image Data  Valuable information could be mined.  Important role of Image Data Mining  Content Based Image Retrieval.  Social network analyzing.  Behavioral advertising.
  4. 4. Outsourcing them to Cloud  Enormous workload on image processing tasks.  How about outsourcing them to cloud?  Cloud: Flexible usage of economical computation resources.
  5. 5. The Problem is the Privacy  Privacy leakage  Outsourced image reveals private info[1].  Various users’ requirements  Sensitivity based on the image content.  Location, Person, Text. [1] Huang L C, Chu H C, Lien C Y, et al. Privacy preservation and information security protection for patients’ portable electronic health records[J]. Computers in biology and medicine, 2009, 39(9): 743-750.
  6. 6. Popular Image Processing Algorithm and the privacy  The state of the art focuses on protecting image content[2].  Pixel Values.  Global Features.  e.g. Histogram  Local Features.  e.g. SIFT descriptor [2] Erkin, Z., Franz, M., Guajardo, J., Katzenbeisser, S., Lagendijk, I., & Toft, T. (2009, January). Privacy-preserving face recognition. In Privacy Enhancing Technologies(pp. 235-253). Springer Berlin Heidelberg.
  7. 7. SIFT Algorithm  SIFT is an useful and popular algorithm to detect content features to better enable further image mining applications[3]. [3] Lowe D G. Object recognition from local scale-invariant features. Computer vision, 1999. The proceedings of the seventh IEEE international conference on. Ieee, 1999, 2: 1150-1157.
  8. 8. Recall Lowe’s SIFT  Two main stages  Scale-space Extrema Detection  Descriptor Generation D(x, y,sij )=[G(x, y,kis)-G(x, y,kjs)]*I(x, y) m(x, y) = Diff (LX (x, y,s ))2 + Diff (LY (x, y,s ))2 q(x, y) = tan-1 Diff (LX (x, y,s )) Diff (LY (x, y,s))
  9. 9. Existing Privacy-preserving SIFT Algorithm  Possible solution  Homomorphic Encryption (HE) [4]  Encryption schemes that enable homomorphic operations over ciphertext domain. 𝐸(𝑓) 𝑓 Homomorphic Property: E( a+b ) = E(a) ⊕ E(b). E( a×b ) = E(a) ⊗ E(b). [4] Hsu, Chao-Yung, Chun-Shien Lu, and Soo-Chang Pei. "Secure and robust SIFT."Proceedings of the 17th ACM international conference on Multimedia. ACM, 2009.
  10. 10. Limitation of HE-based solutions  Limitations of existing HE-based solutions  Functionality  Complicated computation like local features, e.g. SIFT.  Only protecting pixel values.  Performance  Computational complexity.  No existing practical solutions.
  11. 11. Key Ideas  Balance the tradeoff between utility and privacy  Reduce complexity.  Divide the cloud into multiple independent entities to overcome the limitation of HE scheme.  Improve privacy protection  Not only protecting pixel values is not enough.  Protecting location of feature point.  Shape of Objects in image
  12. 12. SecSIFT: A Secure SIFT feature detection system based on Cloud  We propose a privacy-preserving solution to cloud-based computation framework of SIFT.  We employ secure multiparty computation techniques integrated with SIFT computation.  Provide fine-grained privacy definition  Enable practical functionality  Achieve efficient performance
  13. 13. SecSIFT: Framework  We divide the original SIFT algorithm into three stages.  Three entities: Client, Generators, and Comparer.
  14. 14. SecSIFT: Image encryption on Client  Client  Encryption system
  15. 15. SecSIFT: Scale-space Cube Generation  Generator  Scale-space Generation  Cube Encryption  Cube Permutation: Privacy  Noise Perturbation: Effectiveness  Order Preserving Encryption (OPE) and Permutation OPE properties: For all i, j, E(i)>E(j), iff i>j
  16. 16. SecSIFT: Keypoint Discovering  Comparer  Partially recover the encrypted cubes.  Return extremes’ id with dummy ids. OPE Permutation OPE Permutation Insert Dummy IDs
  17. 17. SecSIFT: Descriptor Generation  Generator  We utilize four vectors in fixed directions to approximate the original sift feature vector.
  18. 18. SecSIFT: Experimental Evaluation  Utility  Precision of SecSIFT descriptors  Location of interesting points.  Image matching results.  Feasibility  Efficiency of SecSIFT system  Time complexity.  Workload Distribution.  Privacy  Confidentiality of encrypted value.  Delocalization of interesting points.
  19. 19. SecSIFT: Precision  Euclidean distance between the corresponding keypoints.
  20. 20. SecSIFT: Precision  Error rate of image matching
  21. 21. SecSIFT: Efficiency  Computation time SecSIFT HE-SIFT
  22. 22. SecSIFT: Efficiency  Workload Distribution
  23. 23. SecSIFT: Privacy  Confidentiality of pixel values & descriptors.  One time pad.  Order preserving encryption.  Delocalization of interesting point.  The result shows a quantitative method  E.g. Prob.=0.15 provides privacy equivalent to what appears intended by the HIPAA safe harbor rules. Pr[ExpM,N z (A) =1]= 4z M - z+1 Pr[Expr,d z (A) =1]= |r | | r |+ | d |
  24. 24. Conclusion  SecSIFT: a novel approach that integrates SMC and OPE to enable secure image computation outsourcing with practical performance.  The privacy of the image content is well- defined and protected against cloud.  The performance of SecSIFT is much more efficient than HE-based existing works.
  25. 25. Thank You

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