Managing Privacy Data in Pervasive Camera Networks

Managing Privacy Data in Pervasive Camera Networks Jithendra K. Paruchuri Department of ECE University of Kentucky Lexington, KY - 40508 Thinh P. Nguyen Department of EECS Oregon State University Corvallis, OR - 97330 Sen-ching S. Cheung Department of ECE University of Kentucky Lexington, KY - 40508 ICIP Oct 14 2008

Outline • Motivation and Problem • System Overview • Three Agent Architecture • Reversible Data Hiding Framework • Results • Conclusion & Future Work www.vis.uky.edu | Dedicated to Research, Education and Industrial Outreach | 859.257.1257

Privacy in Pervasive Camera Networks www.vis.uky.edu | Dedicated to Research, Education and Industrial Outreach | 859.257.1257

Client A Client B Client C Privacy Protection Privacy Data Flow Subject A Privacy Data Management System Subject B Subject C Key question 1: How does a client know which subject to ask? Key question 2: How to manage the privacy information in a secure and efficient manner? www.vis.uky.edu | Dedicated to Research, Education and Industrial Outreach | 859.257.1257

Contributions • Retrieval of Privacy Data • Software Agents Architecture • Efficient : No video data exchange • Robustness: No states in agents • Security: No centralized server • Privacy Data Preservation • Reversible Data-Hiding in Compressed Domain • R-D optimized Embedding www.vis.uky.edu | Dedicated to Research, Education and Industrial Outreach | 859.257.1257

Privacy Protecting System RFID Tracking System Key Generation Camera System Object Identification Encryption Object Removal & Obfuscation Data Hiding Video Database Mediator Agent Request Request Permission Permission Client Agent Subject Agent www.vis.uky.edu | Dedicated to Research, Education and Industrial Outreach | 859.257.1257

Step 3 S RSA(K; PKS) TOC: RSA(K;PKC) RSA(K;PKC) Step 6 Step 7 RSA(K; PKC) Step 5 Three Agent Architecture Mediator Agent PKM,SKM Step 2 Step 1 Step 4 Client’s Request S S RSA(K; PKS) RSA(K; PKS) RSA(K; PKS) RSA(TOC; PKm) RSA(TOC; PKm) AES(VS; K) • • • • Subject Agent PKS,SKS Client Agent PKC,SKC AES(VS; K)

Privacy Data Preservation • Cryptographic Scrambling [Boult05], [Dufaux06] • Data Hiding in DCT [Zhang05] • Joint Optimization of Data Hiding & Video Compression [Paruchuri08]

Parity Embedding Last decoded frame DCT Perceptual Mask • DCT Domain • Frequency, contrast and • luminance masking [Watson] Data Hiding with Compression DCT Entropy Coding Motion Compensation www.vis.uky.edu | Dedicated to Research, Education and Industrial Outreach | 859.257.1257

R-D optimized Data Hiding Parity Embedding Last decoded frame Positions of the “optimal’ DCT coeff for embedding DCT R-D Optimization Perceptual Mask • DCT Domain • Frequency, contrast and • luminance masking [Watson] DCT Entropy Coding Reversible Embedding Motion Compensation H.263 H.263 www.vis.uky.edu | Dedicated to Research, Education and Industrial Outreach | 859.257.1257

Reversible Embedding • Authentication: Authorized decoder can perfectly reverse image modifications • Problem with Motion loop • Original reference frame unavailable to regular decoder • Hidden data included in feedback loop • Irreversible effect after quantization • Use Motion-JPEG instead

Reversible Embedding [Ni et al. 03] DCT Residue Histogram • Embedding only at zero location • DCT Residue is Laplacian distributed

Combined rate- distortion cost C(x) # embedded bits R-D based Data Hiding Block-based Rate-Distortion Calculation Psycho-visual Model in DCT [Watson 93] Embedded bit allocation among DCT blocks Solve constrained optimization

Constrained Optimization Let Rk(Mk) and Dk(Mk) be the rate and distortion after hiding Mk bits into the k-th DCT block. δ is a user-defined weight Optimization Problem:

Calculations of Rk(Mk) and Dk(Mk) • Two Issues: • Parity embedding depends on hidden data • Need to find optimal selection within block • “Worst-case” embedding on previous frame • Optimal selection • Complicated by run-length coding • Fast: high to low frequency embedding • Slow: Greedy optimization within block • Very slow: Exhaustive search with DP

Dual of the optimization • Lower bounded by unconstrained opt: • Search λ to meet constraint • Start at 2nd order approximation of Ck(Mk) • 3-5 seconds per one CIF frame

Hall Monitor (QP=10) - MJPEG www.vis.uky.edu | Dedicated to Research, Education and Industrial Outreach | 859.257.1257

Hall Monitor (QP=10) Original Distortion Optimized Weight = 0.5 Rate Optimized

Hall Monitor vs. QP at δ = 0.5 www.vis.uky.edu | Dedicated to Research, Education and Industrial Outreach | 859.257.1257

Conclusions • Privacy System framework • Privacy Data Management Architecture • Privacy Data Preservation by Data Hiding • Current work • Reversible Data Embedding for Scalable Video • Distributed Architecture Privacy Data Management • Incorporate temporal dimension in perceptual and rate model • Perceptual evaluation www.vis.uky.edu | Dedicated to Research, Education and Industrial Outreach | 859.257.1257

Managing Privacy Data in Pervasive Camera Networks