Privacy-leakage Rates Research Articles

Biometric Identification Systems with Both Chosen and Generated Secret Keys by Allowing Correlation

We propose a biometric identification system where the chosen- and generated-secret keys are used simultaneously, and investigate its fundamental limits from information theoretic perspectives. The system consists of two phases: enrollment and identification phases. In the enrollment phase, for each user, the encoder uses a secret key, which is chosen independently, and the biometric identifier to generate another secret key and a helper data. In the identification phase, observing the biometric sequence of the identified user, the decoder estimates index, chosen- and generated-secret keys of the identified user based on the helper data stored in the system database. In this study, the capacity region of such system is characterized. In the problem settings, we allow chosen- and generated-secret keys to be correlated. As a result, by permitting the correlation of the two secret keys, the sum rate of the identification, chosen- and generated-secret key rates can achieve a larger value compared to the case where the keys do not correlate. Moreover, the minimum amount of the storage rate changes in accordance with both the identification and chosen-secret key rates, but that of the privacy-leakage rate depends only on the identification rate.

SocialGuard: An adversarial example based privacy-preserving technique for social images

People are always interested in sharing photos on social platforms. However, undesirable privacy leakage may occur due to such online photo sharing. Advanced deep neural network (DNN) based object detectors can easily steal users’ personal information exposed in shared photos. In this paper, we propose a novel adversarial example based privacy-preserving technique for social images against object detectors-based privacy stealing. Specifically, we propose an Object Disappearance Algorithm to craft two kinds of adversarial social images to fool the object detector. One can hide all the objects in the social images from being detected by an object detector, and the other can make the customized sensitive objects be misclassified by the object detector. Experimental results show that, the proposed method can effectively protect the privacy of social images, while the quality of these images is not affected. The privacy-preserving success rates of the proposed method on MS-COCO and PASCAL VOC 2007 datasets are high up to 96.1% and 99.3%, respectively, and the privacy leakage rates on these two datasets are as low as 0.57% and 0.07%, respectively. Compared with common image processing methods (low brightness, noise, blur, mosaic and JPEG compression) and the existing work, the proposed method can achieve much more powerful performance in protecting the privacy of social images, while not affecting the quality of social images.

Biometric Identification Systems with Noisy Enrollment for Gaussian Sources and Channels.

In the present paper, we investigate the fundamental trade-off of identification, secret-key, storage, and privacy-leakage rates in biometric identification systems for remote or hidden Gaussian sources. We use a technique of converting the system to one where the data flow is in one-way direction to derive the capacity region of these rates. Also, we provide numerical calculations of three different examples for the system. The numerical results imply that it seems hard to achieve both high secret-key and small privacy-leakage rates simultaneously.

Privacy, Secrecy, and Storage With Multiple Noisy Measurements of Identifiers

The key-leakage-storage region is derived for a generalization of a classic two-terminal key agreement model. The additions to the model are that the encoder observes a hidden, or noisy, version of the identifier, and that the encoder and decoder can perform multiple measurements. To illustrate the behavior of the region, the theory is applied to binary identifiers and noise modeled via binary symmetric channels. In particular, the key-leakage-storage region is simplified by applying Mrs. Gerber’s lemma twice in different directions to a Markov chain. The growth in the region as the number of measurements increases is quantified. The amount by which the privacy-leakage rate reduces for a hidden identifier as compared to a noise-free (visible) identifier at the encoder is also given. If the encoder incorrectly models the source as visible, it is shown that substantial secrecy leakage may occur and the reliability of the reconstructed key might decrease.

Controllable Identifier Measurements for Private Authentication With Secret Keys

The problem of secret-key based authentication under a privacy constraint on the source sequence is considered. The identifier measurements during authentication are assumed to be controllable via a cost-constrained "action" sequence. Single-letter characterizations of the optimal trade-off among the secret-key rate, storage rate, privacy-leakage rate, and action cost are given for the four problems where noisy or noiseless measurements of the source are enrolled to generate or embed secret keys. The results are relevant for several user-authentication scenarios including physical and biometric authentications with multiple measurements. Our results include, as special cases, new results for secret-key generation and embedding with action-dependent side information without any privacy constraint on the enrolled source sequence.

Secure and Reliable Key Agreement with Physical Unclonable Functions.

Different transforms used in binding a secret key to correlated physical-identifier outputs are compared. Decorrelation efficiency is the metric used to determine transforms that give highly-uncorrelated outputs. Scalar quantizers are applied to transform outputs to extract uniformly distributed bit sequences to which secret keys are bound. A set of transforms that perform well in terms of the decorrelation efficiency is applied to ring oscillator (RO) outputs to improve the uniqueness and reliability of extracted bit sequences, to reduce the hardware area and information leakage about the key and RO outputs, and to maximize the secret-key length. Low-complexity error-correction codes are proposed to illustrate two complete key-binding systems with perfect secrecy, and better secret-key and privacy-leakage rates than existing methods. A reference hardware implementation is also provided to demonstrate that the transform-coding approach occupies a small hardware area.

Fundamental Limits for Privacy-Preserving Biometric Identification Systems That Support Authentication

In this paper, we analyze two types of biometric identification systems with protected templates that also support authentication. In the first system, two terminals observe biometric enrollment and identification sequences of a number of individuals. It is the goal of these terminals to form a common secret for the sequences belonging to the same individual by interchanging public (helper) messages of all individuals such that the information leakage about the secrets from these helper messages is negligible. These secret keys are used for authentication purposes. Moreover, the second terminal should be able to establish the identity of an individual based on the presented biometric identification sequence and helper messages. It is important to realize that biometric data are unique for individuals and cannot be replaced if compromised. Therefore, the helper messages should contain as little as possible information about the biometric data. In the second setting, we consider the first terminal does not generate secret keys from biometric sequences of individuals but chooses them uniformly at random. These keys are conveyed to the second terminal by communicating the corresponding helper messages. In this paper, we determine the fundamental tradeoffs between secret-key, identification, and privacy-leakage rates for both biometric settings.

Biometric Systems: Privacy and Secrecy Aspects

This paper addresses privacy leakage in biometric secrecy systems. Four settings are investigated. The first one is the standard Ahlswede-Csiszar secret-generation setting in which two terminals observe two correlated sequences. They form a common secret by interchanging a public message. This message should only contain a negligible amount of information about the secret, but here, in addition, we require it to leak as little information as possible about the biometric data. For this first case, the fundamental tradeoff between secret-key and privacy-leakage rates is determined. Also for the second setting, in which the secret is not generated but independently chosen, the fundamental secret-key versus privacy-leakage rate balance is found. Settings three and four focus on zero-leakage systems. Here the public message should only contain a negligible amount of information on both the secret and the biometric sequence. To achieve this, a private key is needed, which can only be observed by the terminals. For both the generated-secret and the chosen-secret model, the regions of achievable secret-key versus private-key rate pairs are determined. For all four settings, the fundamental balance is determined for both unconditional and conditional privacy leakage.