Privacy-Preserving Outsourced Media Search Using Secure Sparse Ternary Codes

2019 IEEE International Workshop on Information Forensics and Security (WIFS)

Stanko

Škorić

et al. 2019

Self Cite

We investigate the privacy of two approaches to (biometric) template protection: Helper Data Systems and Sparse Ternary Coding with Ambiguation. In particular, we focus on a privacy property that is often overlooked, namely how much leakage exists about one specific binary property of one component of the feature vector. This property is e.g. the sign or an indicator that a threshold is exceeded.We provide evidence that both approaches are able to protect such sensitive binary variables, and discuss how system parameters need to be set.

Section: The Code Offset Methods [1] [16]mentioning

confidence: 99%

“…G. Sparse Coding with Amibiguation (SCA) [9], [10] Given a sparse ternary vector v = ϕ(x) ∈ {−1, 0, 1} L , the ambiguation mechanism A turns S n randomly chosen zero components of v into a (random) ±1. The resulting ternary vector u = A(v) is stored as enrolment data (see Fig.…”

Section: E Sparse Binary Coding (Sbc)mentioning

confidence: 99%

Single-Component Privacy Guarantees in Helper Data Systems and Sparse Coding with Ambiguation

2019 IEEE International Workshop on Information Forensics and Security (WIFS)

Stanko

Škorić

et al. 2019

Self Cite

“…This work follows the learning-based approach, however, instead of the usual binary representations used in these methods, we focus on sparsity of representations similarly to [18]. This allows us to benefit from the SCA framework [11,19] for ambiguation.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Image Sharing Via Sparsifying Layers on Convolutional Groups

Ferdowsi

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Holotyak

et al. 2020

Self Cite

We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups. We argue that, while compactness is always desired at scale, this need is more severe when trying to furthermore protect the privacy-sensitive content. We therefore encode images, such that, from one hand, representations are stored in the public domain without paying the huge cost of privacy protection, but ambiguated and hence leaking no discernible content from the images, unless a combinatorially-expensive guessing mechanism is available for the attacker. From the other hand, authorized users are provided with very compact keys that can easily be kept secure. This can be used to disambiguate and reconstruct faithfully the corresponding access-granted images. We achieve this with a convolutional autoencoder of our design, where feature maps are passed independently through sparsifying transformations, providing multiple compact codes, each responsible for reconstructing different attributes of the image. The framework is tested on a large-scale database of images with public implementation available.Index Termsprivacy-preserving image sharing, convolutional autoencoders, sparse representation, learned compression, image obfuscation.

“…We do not pretend to be exhaustive in our overview and refer interesting readers to [8]. Recently, a concept of the STCA was proposed that combines and extends the encoding and randomization principles from the informationtheoretic perspectives [9][10][11]. The STCA ensures the protection of both templates and queries in authentication and identification systems against the adversarial reconstruction and clustering [9,10].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Privacy-Sensitive Data from Protected Templates

Rezaeifar

2019 IEEE International Conference on Image Processing (ICIP)

Taran

et al. 2019

Self Cite

In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA). The STCA is a generalization of randomization techniques which includes random projections, lossy quantization, and addition of ambiguization noise to satisfy the privacy-utility trade-off requirements. The theoretical privacy-preserving properties of STCA have been validated on synthetic data. However, the applicability of STCA to real data and potential threats linked to reconstruction based on recent deep reconstruction algorithms are still open problems. Our results demonstrate that STCA still achieves the claimed theoretical performance when facing deep reconstruction attacks for the synthetic i.i.d. data, while for real images special measures are required to guarantee proper protection of the templates.