Robust 3D object watermarking scheme using shape features for copyright protection.

This article utilizes the discrete wavelet transformation to introduce an advanced 3D object watermarking model depending on the characteristics of the object's vertices. The model entails two different phases: integration and extraction. In the integration phase, a novel technique is proposed, which embeds the secret grayscale image three times using both the encrypted pixels and the vertices' coefficients of the original 3D object. In the extraction phase, the secret image is randomly extracted and recaptured using the inverse phase of the integration technique. Four common 3D objects (Stanford bunny, horse, cat figurine, and angel), with different faces and different vertices, are used in this model as a dataset. The performance of the proposed technique is evaluated using different metrics to show its superiority in terms of execution time and imperceptibility. The results demonstrated that the proposed method achieved high imperceptibility and transparency with low distortion. Moreover, the extracted secret grayscale image perfectly matched the original watermark with a structural similarity index of 1 for all testing models.

MAN-C: A masked autoencoder neural cryptography based encryption scheme for CT scan images.

Sharing medical images securely is very important towards keeping patients' data confidential. In this paper we propose MAN-C: a Masked Autoencoder Neural Cryptography based encryption scheme for sharing medical images. The proposed technique builds upon recently proposed masked autoencoders. In the original paper, the masked autoencoders are used as scalable self-supervised learners for computer vision which reconstruct portions of originally patched images. Here, the facility to obfuscate portions of input image and the ability to reconstruct original images is used an encryption-decryption scheme. In the final form, masked autoencoders are combined with neural cryptography consisting of a tree parity machine and Shamir Scheme for secret image sharing. The proposed technique MAN-C helps to recover the loss in image due to noise during secret sharing of image.•Uses recently proposed masked autoencoders, originally designed as scalable self-supervised learners for computer vision, in an encryption-decryption setup.•Combines autoencoders with neural cryptography - the advantage our proposed approach offers over existing technique is that (i) Neural cryptography is a new type of public key cryptography that is not based on number theory, requires less computing time and memory and is non-deterministic in nature, (ii) masked auto-encoders provide additional level of obfuscation through their deep learning architecture.•The proposed scheme was evaluated on dataset consisting of CT scans made public by The Cancer Imaging Archive (TCIA). The proposed method produces better RMSE values between the input the encrypted image and comparable correlation values between the input and the output image with respect to the existing techniques.

Leah Z. Rand, Daniel P. Carpenter, Aaron S. Kesselheim, Anushka Bhaskar, Jonathan J. Darrow, and William B. Feldman Reply.

The authors respond to a letter by Mitchell Berger in the March-April 2024 issue of the Hastings Center Report concerning their essay "Securing the Trustworthiness of the FDA to Build Public Trust in Vaccines."

Enhancing Laryngeal Spinocellular Carcinoma Image Security with DCT.

This work proposes a novel secret sharing scheme to enhance the security of Laryngeal Spinocellular Carcinoma or Laryngeal Squamous Cell Carcinoma (LSCC) images using the Discrete Cosine Transformation (DCT) as a cryptographic tool. The DCT-based secret sharing method divides LSCC images into shares, each containing DCT coefficients that represent the image's frequency components. The original image can only be reconstructed when a predefined number of shares are combined, ensuring confidentiality and preventing unauthorized access. The proposed scheme demonstrates robustness against noise and data loss during transmission, preserving image quality and data integrity. The performance of the proposed scheme concerning the quality of the recovered image and the strength of security preservation is demonstrated through PSNR improvement analysis, correlation analysis, and histogram analysis. The efficiency of DCT-based secret sharing enables application in medical settings, facilitating accurate diagnosis and treatment planning for LSCC patients while safeguarding patient privacy.

Dynamic Hyperspectral Holography Enabled by Inverse-Designed Metasurfaces with Oblique Helicoidal Cholesterics.

Metasurfaces are flat arrays of nanostructures that allow exquisite control of phase and amplitude of incident light. Although metasurfaces offer new active element for both fundamental science and applications, the challenge still remains to overcome their low information capacity and passive nature. Here, by integrating an inverse-designed-metasurface with oblique helicoidal cholesteric liquid crystal (ChOH), simultaneous spatial and spectral tunable metasurfaces with a high information capacity for dynamic hyperspectral holography, are demonstrated. The inverse design facilitates a single-phase map encoding of ten independent holographic images at different wavelengths. ChOH provides precise spectral modulation with narrow bandwidth and wide tunable regime in response to programmed stimuli, thus enabling dynamic switching of the multicolor holography. The results provide simple and generalizable principles for the rational design of interactive metasurfaces that will find numerous applications, including security platform.

A global progressive image secret sharing scheme under multi-group joint management.

Diverging from traditional secret sharing schemes, group secret sharing schemes enable the recovery of secret information through collaborative efforts among groups. Existing schemes seldom consider the issue of the secrecy level of image information between different groups. Therefore, we propose a global progressive image secret sharing scheme under multi-group joint management. For inter-group relations, multiple groups with different priority levels are constructed using the approach of bit-polar decomposition. In this arrangement, higher-level groups obtain clearer secret image information. For intra-group relations, a participant-weighted secret sharing scheme is constructed based on Chinese Remainder Theorem and Birkhoff interpolation, in which the participants' secret sub-shares are reusable. During the recovery process, the sub-images can be recovered within the intragroup with the corresponding level. Groups collaborate through lightweight overlay operations to obtain different layers of secret images, achieving a global progressive effect. Analysis results show that the scheme is both secure and practical for group secret sharing.

Secure Aggregation Protocol Based on DC-Nets and Secret Sharing for Decentralized Federated Learning.

In the era of big data, millions and millions of data are generated every second by different types of devices. Training machine-learning models with these data has become increasingly common. However, the data used for training are often sensitive and may contain information such as medical, banking, or consumer records, for example. These data can cause problems in people's lives if they are leaked and also incur sanctions for companies that leak personal information for any reason. In this context, Federated Learning emerges as a solution to the privacy of personal data. However, even when only the gradients of the local models are shared with the central server, some attacks can reconstruct user data, allowing a malicious server to violate the FL principle, which is to ensure the privacy of local data. We propose a secure aggregation protocol for Decentralized Federated Learning, which does not require a central server to orchestrate the aggregation process. To achieve this, we combined a Multi-Secret-Sharing scheme with a Dining Cryptographers Network. We validate the proposed protocol in simulations using the MNIST handwritten digits dataset. This protocol achieves results comparable to Federated Learning with the FedAvg protocol while adding a layer of privacy to the models. Furthermore, it obtains a timing performance that does not significantly affect the total training time, unlike protocols that use Homomorphic Encryption.

SecBERT: Privacy-preserving pre-training based neural network inference system.

Pre-trained models such as BERT have made great achievements in natural language processing tasks in recent years. In this paper, we investigate the privacy-preserving pre-training based neural network inference in a two-server framework based on additive secret sharing technique. Our protocol allows a resource-restrained client to request two powerful servers to cooperatively process the natural processing tasks without revealing any useful information about its data. We first design a series of secure sub-protocols for non-linear functions used in BERT model. These sub-protocols are expected to have broad applications and of independent interest. Based on the building sub-protocols, we propose SecBERT, a privacy-preserving pre-training based neural network inference protocol. SecBERT is the first cryptographically secure privacy-preserving pre-training based neural network inference protocol. We show security, efficiency and accuracy of SecBERT protocol through comprehensive theoretical analysis and experiments.

Dialing for doctors: Secret shopper study of Arizona methadone and buprenorphine providers, 2022.

INTRODUCTION: Methadone and buprenorphine are effective and safe treatments for opioid use disorder (OUD) and also reduce overdose and all-cause mortality. Identifying and reaching providers of medication for opioid use disorder (MOUD) has proven difficult for prospective patients and researchers. OBJECTIVES: To assess the accuracy of government-maintained lists of Arizona (AZ) providers prescribing MOUD, and the extent to which these providers are accessible for treatment. METHODS: A two-phase study used a listing of 2376 AZ MOUD providers obtained from the U.S. Drug Enforcement Administration and the Substance Abuse and Mental Health Services Administration. Phase 1 assessed the accuracy of the listing using internet confirmatory research from May-October 2022. Phase 2 used the resulting list of 838 providers to assess provider availability, type of MOUD treatment provided, and accepted payment through secret shopper calls between November 16 and 30, 2022. RESULTS: Just over half (52.2 %, n = 1240) of providers were removed from the original listing during Phase 1. One quarter (25.9 %) were no longer in practice. Among the 833 eligible for the secret shopper Phase 2 study, 36.6 % (n = 307) were reached and identified as providing MOUD. A vast majority (88.1 %) of MOUD providers indicating treatment type were accepting new patients, however methadone was identified far more frequently than was likely permitted or provided for OUD. Providers were 5.5 times more likely to accept new patients if they accepted cash payment for services, and 4.9 times more likely if they accepted Medicaid. Rural areas remained underserved. CONCLUSIONS: The active population of MOUD providers is far smaller than surmised. DEA and SAMHSA provider listings are not sufficiently accurate for survey research sampling. Other means of representative sampling will need to be devised, and trusted lists of providers for prospective patients should be promoted, publicly available, and regularly maintained for accuracy. Providers that offer treatment should assure that public-facing staff have basic information about the practice, the treatment offered, and conditions for taking new patients. Concerted efforts must assure rural access at the most local levels to reduce patient travel burden.

[François Gros: a leading figure, a secretive man]. / François Gros : Une personnalité de premier plan, un homme secret.

I joined François Gros' laboratory in 1975, to study mechanisms of gene expression in eukaryotes. Despite the lack of powerful tools, that would be brought later by genetic engineering, I obtained publishable results and was allowed to defend a third cycle thesis. Thereafter, I joined Margaret Buckingham's group, which was empowering within François' laboratory. I maintained regular meetings with François, a leading figure but a secretive man, who did not readily open up. It was my privilege, over the more than 45 years I have been around him, to have glimpses over what had been really significant to him. This has been a rich and very precious experience.

J'ai rejoint le laboratoire de François Gros en 1975, pour étudier les mécanismes de l'expression génétique chez les eucaryotes. Malgré la carence en outils performants, qu'allait apporter le génie génétique, j'ai obtenu des résultats publiables et pu soutenir une thèse de 3 e cycle. Après cela, j'ai rejoint le groupe de Margaret Buckingham, qui s'autonomisait dans le laboratoire de François. J'ai continué à avoir des rencontres régulières avec François, personnalité de premier plan mais homme secret, qui ne se livrait pas volontiers. J'ai eu le privilège, au cours des 45 ans et plus où je l'ai côtoyé, d'avoir quelques aperçus de ce qui l'avait marqué, l'avait formé, lui importait vraiment. Ça été une expérience riche et très précieuse.

A Numerical Study on the Capacity Region of a Three-Layer Wiretap Network.

In this paper, we study a three-layer wiretap network including the source node in the top layer, N nodes in the middle layer and L sink nodes in the bottom layer. Each sink node recovers the message generated from the source node correctly via the middle layer nodes that it has access to. Furthermore, it is required that an eavesdropper eavesdropping a subset of the channels between the top layer and the middle layer learns absolutely nothing about the message. For each pair of decoding and eavesdropping patterns, we are interested in finding the capacity region consisting of (N+1)-tuples, with the first element being the size of the message successfully transmitted and the remaining elements being the capacity of the N channels from the source node to the middle layer nodes. This problem can be seen as a generalization of the secret sharing problem. We show that when the number of middle layer nodes is no larger than four, the capacity region is fully characterized as a polyhedral cone. When such a number is 5, we find the capacity regions for 74,222 decoding and eavesdropping patterns. For the remaining 274 cases, linear capacity regions are found. The proving steps are: (1) Characterizing the Shannon region, an outer bound of the capacity region; (2) Characterizing the common information region, an outer bound of the linear capacity region; (3) Finding linear schemes that achieve the Shannon region or the common information region.

Two-level QR code scheme based on region matrix image secret sharing algorithm.

Quick response (QR) codes have become increasingly popular as a medium for quickly and easily accessing information through mobile devices. However, the open-source nature of QR code encoding poses a risk of information leakage and potential attacks, especially with the growing use of QR codes in financial services and authentication applications. To mitigate the risk of information leakage due to open-source QR code encoding, this paper proposes a two-level QR code scheme based on a region matrix image secret sharing algorithm. In this scheme, the first-level public information can be directly obtained by scanning with any standard QR code scanner, while the two-level secret information can only be accessed by overlaying the shared images. To enhance the robustness of joint secret information recovery using shared images, this article designs a progressive image secret sharing algorithm based on region matrices. This algorithm meticulously processes high-priority share regions and generates multiple substitute shares. In the event of attacks on key shares, substitute shares can be employed to recover the secret information. For an increased payload capacity of each QR code, an adaptive pixel depth adjustment algorithm is devised. This algorithm ensures that the recovery of two-level secret information maintains high clarity, while not affecting the scanning functionality of each shared QR code. Experimental results validate the feasibility of this scheme, which simplifies the construction matrix, reduces matrix redundancy, and exhibits priority partitioning and higher robustness. Furthermore, QR codes embedding secret shares can safeguard the two-level information, and the recovered images exhibit exceptional clarity.

Creating Digital Watermarks in Bitmap Images Using Lagrange Interpolation and Bezier Curves.

The article is devoted to the introduction of digital watermarks, which formthe basis for copyright protection systems. Methods in this area are aimed at embedding hidden markers that are resistant to various container transformations. This paper proposes a method for embedding a digital watermark into bitmap images using Lagrange interpolation and the Bezier curve formula for five points, called Lagrange interpolation along the Bezier curve 5 (LIBC5). As a means of steganalysis, the RS method was used, which uses a sensitive method of double statistics obtained on the basis of spatial correlations in images. The output value of the RS analysis is the estimated length of the message in the image under study. The stability of the developed LIBC5 method to the detection of message transmission by the RS method has been experimentally determined. The developed method proved to be resistant to RS analysis. A study of the LIBC5 method showed an improvement in quilting resistance compared to that of the INMI image embedding method, which also uses Lagrange interpolation. Thus, the LIBC5 stegosystem can be successfully used to protect confidential data and copyrights.

Multi-Sensing Platform Based on 2D Monoelement Germanane.

Covalently functionalized germanane is a novel type of fluorescent probe that can be employed in material science and analytical sensing. Here, a fluorometric sensing platform based on methyl-functionalized germanane (CH3 Ge) is developed for gas (humidity and ammonia) sensing, pH (1-9) sensing, and anti-counterfeiting. Luminescence (red-orange) is seen when a gas molecule intercalates into the interlayer space of CH3 Ge and the luminescence disappears upon deintercalation. This allows for direct detection of gas absorption via fluorometric measurements of the CH3 Ge. Structural and optical properties of CH3 Ge with intercalated gas molecules are investigated by density functional theory (DFT). To demonstrate real-time and on-the-spot testing, absorbed gas molecules are first precisely quantified by CH3 Ge using a smartphone camera with an installed color intensity processing application (APP). Further, CH3 Ge-paper-based sensor is integrated into real food packets (e.g., fish and milk) to monitor the shelf life of perishable foods. Finally, CH3 Ge-based rewritable paper is applied in water jet printing to illustrate the potential for secret communication with quick coloration and good reversibility by water evaporation.

[The role of the nursery nurse in supporting newborns born in secret during the neonatal period]. / Place de l'infirmière puéricultrice dans l'accompagnement du nouveau-né né sous le secret en période néonatale.

To be born secretly is to be born without filiation, to experience abandonment, early separation and rupture. Birth in secret is governed by numerous laws designed to protect both mother and child. It is important to specify that newborns born in secret have specific needs that must be met. That's why the support provided by the nursery nurse is essential.

Analysis of the Mutual Information of Channel Phase Observations in Line-of-Sight Scenarios.

The mutual information of the observed channel phase between devices can serve as an entropy source for secret key generation in line-of-sight scenarios. However, so far only simulated and numeric results were available. This paper derives the probability distribution of the channel phase and corresponding expressions for the mutual information. Moreover, the orientation distribution is optimized in order to maximize the mutual information. All presented results are validated numerically. These outcomes serve as a basis for further analytic investigations on the secret key generation rate and subsequent physical layer security performance analysis in line-of-sight scenarios, such as those encountered in drone-aided communications.

Lossless Reversible Data Hiding in Encrypted Image for Multiple Data Hiders Based on Pixel Value Order and Secret Sharing.

Reversible data hiding in encrypted images (RDH-EI) is instrumental in image privacy protection and data embedding. However, conventional RDH-EI models, involving image providers, data hiders, and receivers, limit the number of data hiders to one, which restricts its applicability in scenarios requiring multiple data embedders. Therefore, the need for an RDH-EI accommodating multiple data hiders, especially for copyright protection, has become crucial. Addressing this, we introduce the application of Pixel Value Order (PVO) technology to encrypted reversible data hiding, combined with the secret image sharing (SIS) scheme. This creates a novel scheme, PVO, Chaotic System, Secret Sharing-based Reversible Data Hiding in Encrypted Image (PCSRDH-EI), which satisfies the (k,n) threshold property. An image is partitioned into N shadow images, and reconstruction is feasible when at least k shadow images are available. This method enables separate data extraction and image decryption. Our scheme combines stream encryption, based on chaotic systems, with secret sharing, underpinned by the Chinese remainder theorem (CRT), ensuring secure secret sharing. Empirical tests show that PCSRDH-EI can reach a maximum embedding rate of 5.706 bpp, outperforming the state-of-the-art and demonstrating superior encryption effects.

The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.

BACKGROUND: Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures. OBJECTIVE: Various tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond. METHODS: The FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime. RESULTS: FeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites. CONCLUSIONS: FeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond.

An anti-collusion attack defense method for physical layer key generation scheme based on transmission delay.

Physical layer security (PLS) is considered one of the most promising solutions to solve the security problems of massive Internet of Things (IoTs) devices because of its lightweight and high efficiency. Significantly, the recent physical layer key generation (PLKG) scheme based on transmission delay proposed by Huang et al. (2021) does not have any restrictions on communication methods and can extend the traditional physical layer security based on wireless channels to the whole Internet scene. However, the secret-sharing strategy adopted in this scheme has hidden dangers of collusion attack, which may lead to security problems such as information tampering and privacy disclosure. By establishing a probability model, this article quantitatively analyzes the relationship between the number of malicious collusion nodes and the probability of key exposure, which proves the existence of this security problem. In order to solve the problem of collusion attack in Huang et al.'s scheme, this article proposes an anti-collusion attack defense method, which minimizes the influence of collusion attack on key security by optimizing parameters including the number of the middle forwarding nodes, the random forwarding times, the time delay measurement times and the out-of-control rate of forwarding nodes. Finally, based on the game model, we prove that the defense method proposed in this article can reduce the risk of key leakage to zero under the scenario of the "Careless Defender" and "Cautious Defender" respectively.