Strong Secrecy Research Articles

Differential Privacy Protection of Face Images Based on Region Growing

Face images, as an information carrier, are rich in sensitive information. Direct publication of these images would cause privacy leak, due to their natural weak privacy. Most of the existing privacy protection methods for face images adopt data publication under a non-interactive framework. However, the E-effect under this framework covers the entire image, such that the noise influence is uniform across the image. To solve the problem, this paper proposes region growing publication (RGP), an algorithm for the interactive publication of face images under differential privacy. This innovative algorithm combines the region growing technique with differential privacy technique. The privacy budget E is dynamically allocated, and the Laplace noise is added, according to the similarity between adjacent sub-images. To measure this similarity more effectively, the fusion similarity measurement mechanism (FSMM) was designed, which better adapts to the intrinsic attributes of images. Different from traditional region growing rules, the FSMM fully considers various attributes of images, including brightness, contrast, structure, color, texture, and spatial distribution. To further enhance algorithm feasibility, RGP was extended to atypical region growing publication (ARGP). While RGP limits the region growing direction between adjacent sub-images, ARGP searches for the qualified sub-images across the image, with the aid of the exponential mechanism, thereby expanding the region merging scope of the seed point. The results show that our algorithm can satisfy E-differential privacy, and the denoised image still have a high availability.

Social Welfare Analysis under Different Levels of Consumers’ Privacy Regulation

With the rapid development of information technology, digital platforms can collect, utilize, and share large amounts of specific information of consumers. However, these behaviors may endanger information security, thus causing privacy concerns among consumers. Considering the information sharing among firms, this paper constructs a two-period duopoly price competition Hotelling model, and gives insight into the impact of three different levels of privacy regulations on industry profit, consumer surplus, and social welfare. The results show that strong privacy protection does not necessarily make consumers better off, and weak privacy protection does not necessarily hurt consumers. Information sharing among firms will lead to strong competitive effects, which will prompt firms to lower the price for new customers, thus damaging the profits of firms, and making consumers’ surplus higher. The level of social welfare under different privacy regulations depends on consumers’ product-privacy preference, and the cost of information coordination among firms. With the cost of information coordination among firms increasing, it is only in areas where consumers have greater privacy preferences that social welfare may be optimal under the weak regulation.

Quantifying identifiability to choose and audit ϵ in differentially private deep learning

Differential privacy allows bounding the influence that training data records have on a machine learning model. To use differential privacy in machine learning, data scientists must choose privacy parameters (ϵ, δ ). Choosing meaningful privacy parameters is key, since models trained with weak privacy parameters might result in excessive privacy leakage, while strong privacy parameters might overly degrade model utility. However, privacy parameter values are difficult to choose for two main reasons. First, the theoretical upper bound on privacy loss (ϵ, δ) might be loose, depending on the chosen sensitivity and data distribution of practical datasets. Second, legal requirements and societal norms for anonymization often refer to individual identifiability, to which (ϵ, δ ) are only indirectly related. We transform (ϵ, δ ) to a bound on the Bayesian posterior belief of the adversary assumed by differential privacy concerning the presence of any record in the training dataset. The bound holds for multidimensional queries under composition, and we show that it can be tight in practice. Furthermore, we derive an identifiability bound, which relates the adversary assumed in differential privacy to previous work on membership inference adversaries. We formulate an implementation of this differential privacy adversary that allows data scientists to audit model training and compute empirical identifiability scores and empirical (ϵ, δ ).

Secure Polar Coding for the Primitive Relay Wiretap Channel.

With the emergence of wireless networks, cooperation for secrecy is recognized as an attractive way to establish secure communications. Departing from cryptographic techniques, secrecy can be provided by exploiting the wireless channel characteristics; that is, some error-correcting codes besides reliability have been shown to achieve information-theoretic security. In this paper, we propose a polar-coding-based technique for the primitive relay wiretap channel and show that this technique is suitable to provide information-theoretic security. Specifically, we integrate at the relay an additional functionality, which allows it to smartly decide whether it will cooperate or not based on the decoding detector result. In the case of cooperation, the relay operates in a decode-and-forward mode and assists the communication by transmitting a complementary message to the destination in order to correctly decode the initial source’s message. Otherwise, the communication is completed with direct transmission from source to the destination. Finally, we first prove that the proposed encoding scheme achieves weak secrecy, then, in order to overcome the obstacle of misaligned bits, we implement a double-chaining construction, which achieves strong secrecy.

Weak Privacy, Weak Procurement: The State of Facial Recognition in Canada

Tech companies and law enforcement are taking advantage of weak legal regimes in Canada to increase capital and power through the pretext of public safety.

Efficient neural networks for edge devices

Due to limited computation and storage resources of industrial internet of things (IoT) edge devices, many emerging intelligent industrial IoT applications based on deep neural networks (DNNs) heavily depend on cloud computing for computation and storage. However, cloud computing faces technical issues in long latency, poor reliability, and weak privacy, resulting in the need for on-device computation and storage. On-device computation is essential for many time-critical industrial IoT applications, which require real-time data processing. In this paper, we review three major research areas for on-device computation, specifically quantization, pruning, and network architecture design. The three techniques could enable a DNN model to be deployed on edge devices for real-time computation and storage, mainly due to the reduction of computation and space complexity. More importantly, these techniques could make DNNs applicable to industrial IoT devices.

Coded Caching in the Presence of a Wire and a Cache Tapping Adversary of Type II

This paper introduces the notion of cache-tapping into the information theoretic models of coded caching. The wiretap channel II in the presence of multiple receivers equipped with fixed-size cache memories, and an adversary which selects symbols to tap into from cache placement and/or delivery is introduced. The legitimate terminals know neither whether placement, delivery, or both are tapped, nor the positions in which they are tapped. Only the size of the overall tapped set is known. For two receivers and two files, the strong secrecy capacity- the maximum achievable file rate while keeping the overall library strongly secure- is identified. Lower and upper bounds on the strong secrecy file rate are derived when the library has more than two files. Achievability relies on a code design which combines wiretap coding, security embedding codes, one-time pad keys, and coded caching. A genie-aided upper bound, in which the transmitter is provided with user demands before placement, establishes the converse for the two-files case. For more than two files, the upper bound is constructed by three successive channel transformations. Our results establish provable security guarantees against a powerful adversary which optimizes its tapping over both phases of communication in a cache-aided system.

Strong Secrecy of Arbitrarily Varying Multiple Access Channels

This paper investigates the strong secrecy capacity of the arbitrarily varying multiple access channel (AVMAC). First, Csiszár's almost independent coloring lemma is generalized to establish an achievable secrecy rate region of the AVMAC with an eavesdropper, which includes existing results on both the arbitrarily varying wiretap channel and the multiple access wiretap channel. We then determine the capacity for a special case named semi-noiseless wiretap channel. In addition, a multi-letter outer bound is also presented. Finally, the results of this paper are further explained via a binary example with a numerical inner bound.

Fundamental Limits of Biometric Identification System Under Noisy Enrollment

The fundamental limits of biometric identification systems under a strong secrecy criterion are investigated. In the previous studies of this scenario, the fundamental trade-off among secrecy, template, privacy- and secrecy-leakages has been revealed in the case where there is only one user, while the case of multiple users has not been discussed yet. In this study, we consider the system with exponentially many users, and we characterize the capacity region of the rate tuples including the user (identification) rate under the strong secrecy criterion. In the achievability proof, we derive a new method to incorporate multiple users by extending the random binning for one user's case. The obtained result shows that the characterization of the capacity region does not vary regardless of the weak or strong secrecy criterion in terms of secrecy-leakage.

Face Image Publication Based on Differential Privacy

As an information carrier, face images contain abundant sensitive information. Due to its natural weak privacy, direct publishing may divulge privacy. Anonymization Technology and Data Encryption Technology are limited by the background knowledge and attack means of attackers, which cannot completely content the needs of face image privacy protection. Therefore, this paper proposes a face image publishing SWP (sliding window publication) algorithm, which satisfies the differential privacy. Firstly, the SWP translates the image gray matrix into a one‐dimensional ordered data stream by using image segmentation technology. The purpose of this step is to transform the image privacy protection problem into the data stream privacy protection problem. Then, the sliding window model is used to model the data flow. By comparing the similarity of data in adjacent sliding windows, the privacy budget is dynamically allocated, and Laplace noise is added. In SWP, the data in the sliding window comes from the image. To present the image features contained in the data more comprehensively and use the privacy budget more reasonably, this paper proposes a fusion similarity measurement EM (exact mechanism) mechanism and a dynamic privacy budget allocation DA (dynamic allocation) mechanism. Also, for further improving the usability of human face images and reducing the impact of noise, a sort‐SWP algorithm based on the SWP method is proposed in the paper. Through the analysis, it can be seen that ordered input can further improve the usability of the SWP algorithm, but direct sorting of data will destroy the ε‐differential privacy. Therefore, this paper proposes a sorting method‐SAS method, which satisfies the ε‐differential privacy; SAS obtain an initial sort by using an exponential mechanism firstly. And then an approximate correct sort is obtained by using the Annealing algorithm to optimize the initial sort. Compared with LAP algorithm and SWP algorithm, the average accuracy rate of sort‐SWP algorithm in ORL, Yale is increased by 56.63% and 21.55%, the recall rate is increased by 6.85% and 3.32%, and F1‐sroce is improved by 55.62% and 16.55%.

Analysis and resistance of dynamic degradation of digital chaos via functional graphs

To analyze the effects of finite computational precision on the dynamical degradation of digital chaotic maps, we designed a periodic orbit detection algorithm that is based on a functional graph. The algorithm can calculate the transient length of each chaotic orbit, weak secret keys, fixed points and the period distribution of digital chaotic systems with various limited computational precisions accurately. Furthermore, an effective method based on quadratic congruential generator is proposed to counteract the performance degradation of digital chaos. Based on the method, a new chaotic model can be constructed with full cycle. Numerical experiments are carried out to evaluate the feasibility of the proposed scheme in terms of phase portrait, autocorrelation function, complexity and periodicity analysis. Finally, as an application, a pseudorandom bit generator with satisfactory performance is designed for chaotic secure communication.

Multi-Entity and Multi-Enrollment Key Agreement With Correlated Noise

A basic model for key agreement with a remote (or hidden) source is extended to a multi-user model with joint secrecy and privacy constraints over all entities that do not trust each other after key agreement. Multiple entities using different measurements of the same source through broadcast channels (BCs) to agree on mutually-independent local secret keys are considered. Our model is the proper multi-user extension of the basic model since the encoder and decoder pairs are not assumed to trust other pairs after key agreement, unlike assumed in the literature. Strong secrecy constraints imposed on all secret keys jointly, which is more stringent than separate secrecy leakage constraints for each secret key considered in the literature, are satisfied. Inner bounds for maximum key rate, and minimum privacy-leakage and database-storage rates are proposed for any finite number of entities. Inner and outer bounds for degraded and less-noisy BCs are given to illustrate cases with strong privacy. A multi-enrollment model that is used for common physical unclonable functions is also considered to establish inner and outer bounds for key-leakage-storage regions that differ only in the Markov chains imposed. For this special case, the encoder and decoder measurement channels have the same channel transition matrix and secrecy leakage is measured for each secret key separately. We illustrate cases for which it is useful to have multiple enrollments as compared to a single enrollment and vice versa.

Distributed Multi-User Secret Sharing

We consider a distributed secret sharing system that consists of a dealer, n storage nodes, and m users. Each user is given access to a certain subset of storage nodes, where it can download the stored data. The dealer wants to securely convey a specific secret s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> to user j via storage nodes, for j=1,2, ..., m. More specifically, two secrecy conditions are considered in this multi-user context. The weak secrecy condition is that each user does not get any information about the individual secrets of other users, while the perfect secrecy condition implies that a user does not get any information about the collection of all other users' secrets. In this system, the dealer encodes secrets into several secret shares and loads them into the storage nodes. Given a certain number of storage nodes we find the maximum number of users that can be served in such a system and construct schemes that achieve this with perfect secrecy. We further define two major properties for such distributed secret sharing systems; communication complexity is defined as the total amount of data that users need to download in order to reconstruct their secrets; and storage overhead is defined as the total size of data loaded by the dealer into the storage nodes normalized by the total size of secrets. Lower bounds on the minimum communication complexity and the storage overhead are characterized given any n and m. We construct distributed secret sharing protocols, under certain conditions on the system parameters, that attain the lower bound on the communication complexity while providing perfect secrecy. Furthermore, we construct protocols, again under certain conditions, that simultaneously attain the lower bounds on the communication complexity and the storage overhead while providing weak secrecy, thereby demonstrating schemes that are optimal in terms of both parameters. It is shown how to modify the proposed protocols in order to construct schemes with balanced storage load and communication complexity.

Cooperative Resolvability and Secrecy in the Cribbing Multiple-Access Channel

We study channel resolvability for the discrete memoryless multiple-access channel with cribbing, i.e., the characterization of the amount of randomness required at the inputs to approximately produce a chosen i.i.d. output distribution according to Kullback-Leibler divergence. We analyze resolvability rates when one encoder cribs (i) the input of the other encoder; or the output of the other encoder, (ii) non-causally, (iii) causally, or (iv) strictly-causally. For scenarios (i)-(iii), we exactly characterize the channel resolvability region. For (iv), we provide inner and outer bounds for the channel resolvability region; the crux of our achievability result is to handle the strict causality constraint with a block-Markov coding scheme in which dependencies across blocks are suitably hidden. Finally, we leverage the channel resolvability results to derive achievable secrecy rate regions for each of the cribbing scenarios under strong secrecy constraints.

On Secure Communications Over Gaussian Wiretap Channels via Finite-Length Codes

Practical codes for the Gaussian wiretap channel are designed aiming at satisfying information-theoretic metrics to ensure security against a passive eavesdropper (Eve). Specifically, a design criterion is introduced for the coset coding scheme in order to satisfy a strong secrecy condition described with the mutual information between the secret message and Eve's observation. In addition, mutual information neural estimation (MINE) powered from deep learning tools is applied in order to directly compute the information-theoretic security constraint, and verify the proposed solutions. It is shown that finite-length coset codes can indeed ensure secure transmission from an information-theoretic perspective.

Expression Level of Small Envelope Protein in Addition to Sequence Divergence inside Its Major Hydrophilic Region Contributes to More Efficient Surface Antigen Secretion by Hepatitis B Virus Subgenotype D2 than Subgenotype A2.

Hepatitis B surface antigen (HBsAg) promotes persistent hepatitis B virus (HBV) infection. It primarily corresponds to small (S) envelope protein secreted as subviral particles. We previously found that genotype D clones expressed less S protein than genotype A clones but showed higher extracellular/intracellular ratio of HBsAg suggesting more efficient secretion. The current study aimed to characterize the underlying mechanism(s) by comparing a subgenotype A2 clone (geno5.4) with a subgenotype D2 clone (geno1.2). Five types of full-length or subgenomic constructs were transfected to Huh7 cells at different dosage. HBsAg was quantified by enzyme linked immunosorbent assay while envelope proteins were detected by Western blot. We found that ratio of extracellular/intracellular HBsAg decreased at increasing amounts of DNA transfected. Conflicting findings from two types of subgenomic construct confirmed stronger secretion inhibitory effect of the genotype D-derived large envelope protein. Chimeric constructs followed by site-directed mutagenesis revealed geno1.2 specific V118/T127 and F161/A168 in the S protein as promoting and inhibitory of HBsAg secretion, respectively. In conclusion, more efficient HBsAg secretion by subgenotype D2 than subgenotype A2 is attributed to lower level of S protein expression in addition to V118 and T127 in S protein, although its F161 and A168 sequences rather reduce HBsAg secretion.

Enhancing the privacy of negative surveys using negative combined categories

In recent years, the negative survey, which can preserve the privacy of individuals when used for collecting sensitive information, has attracted significant attention. However, the privacy of the typical negative survey is limited by the number of categories. When the number of categories is small, the typical negative survey exhibits weak privacy preservation. In particular, when only two categories exist, the typical negative survey cannot preserve the privacy of individuals. Moreover, at times, the privacy requirements of participants are strict. In such a situation, the typical negative survey fails to provide satisfactory privacy preservation. In this paper, two novel negative survey models that use negative combined categories (NCCs), NCC-I and NCC-II, are proposed. They can provide improved individual privacy preservation, in particular for sensitive information with only two categories. The experimental results demonstrate that the proposed methods can achieve superior privacy preservation and provide accurate reconstructed results.

Fluid catalytic cracking process and apparatus for maximizing light olefin yield and other applications

In recent years, the negative survey, which can preserve the privacy of individuals when used for collecting sensitive information, has attracted significant attention. However, the privacy of the typical negative survey is limited by the number of categories. When the number of categories is small, the typical negative survey exhibits weak privacy preservation. In particular, when only two categories exist, the typical negative survey cannot preserve the privacy of individuals. Moreover, at times, the privacy requirements of participants are strict. In such a situation, the typical negative survey fails to provide satisfactory privacy preservation. In this paper, two novel negative survey models that use negative combined categories (NCCs), NCC-I and NCC-II, are proposed. They can provide improved individual privacy preservation, in particular for sensitive information with only two categories. The experimental results demonstrate that the proposed methods can achieve superior privacy preservation and provide accurate reconstructed results.

Stealthy Secret Key Generation.

In order to make a warden, Willie, unaware of the existence of meaningful communications, there have been different schemes proposed including covert and stealth communications. When legitimate users have no channel advantage over Willie, the legitimate users may need additional secret keys to confuse Willie, if the stealth or covert communication is still possible. However, secret key generation (SKG) may raise Willie’s attention since it has a public discussion, which is observable by Willie. To prevent Willie’s attention, we consider the source model for SKG under a strong secrecy constraint, which has further to fulfill a stealth constraint. Our first contribution is that, if the stochastic dependence between the observations at Alice and Bob fulfills the strict more capable criterion with respect to the stochastic dependence between the observations at Alice and Willie or between Bob and Willie, then a positive stealthy secret key rate is identical to the one without the stealth constraint. Our second contribution is that, if the random variables observed at Alice, Bob, and Willie induced by the common random source form a Markov chain, then the key capacity of the source model SKG with the strong secrecy constraint and the stealth constraint is equal to the key capacity with the strong secrecy constraint, but without the stealth constraint. For the case of fast fading models, a sufficient condition for the existence of an equivalent model, which is degraded, is provided, based on stochastic orders. Furthermore, we present an example to illustrate our results.

Polar Coding for the Multiple Access Channel With Confidential Messages

The multiple access channel with confidential messages (MAC-CM) generalizes the traditional multiple access channel by allowing both users to receive channel outputs. Achievable secrecy rate region of this model has been established using random coding arguments, whereas practical codes design remains to be further discussed. In this paper, we develop an explicit polar coding scheme that achieves the secrecy rate region of the MAC-CM under strong secrecy. In particular, our scheme mainly relies on monotone chain rules and polar coding techniques for single-user channels. A proper chaining scheme is constructed to align polar indices and to provide strong secrecy. Finally, a rigorous analysis is provided to validate the performance of our scheme.