The use of encryption for medical images offers several benefits. Firstly, it enhances the confidentiality and privacy of patient data, preventing unauthorized individuals or entities from accessing sensitive medical information. Secondly, encrypted medical images may be sent securely via unreliable networks, like the Internet, without running the danger of data eavesdropping or tampering. Traditional methods of storing and retrieving medical images often lack efficient encryption and privacy-preserving mechanisms. This project delves into enhancing the security and accessibility of medical image storage across diverse cloud environments. Through the implementation of encryption methods, pixel scrambling techniques, and integration with AWS S3, the research aimed to fortify the confidentiality of medical images while ensuring rapid retrieval. These findings collectively illuminate the security, and operational efficiency of the implemented encryption, scrambling techniques, AWS integration, and offer a foundation for advancing secure medical image retrieval in multi-cloud settings.

Data security in numerous businesses, including banking, healthcare, and transportation, depends on cryptography. As IoT and AI applications proliferate, this is becoming more and more evident. Despite the benefits and drawbacks of traditional cryptographic methods such as symmetric and asymmetric encryption, there remains a demand for enhanced security that does not compromise efficiency. This work introduces a novel approach called Multi-fused cryptography, which combines the benefits of distinct cryptographic methods in order to overcome their shortcomings. Through a comparative performance analysis; our study demonstrates that the proposed technique successfully enhances data security during network transmission.

In this work, we leverage the pure skin color patch from the face image as the additional information to train an auxiliary skin color feature extractor and face recognition model in parallel to improve performance of state-of-the-art (SOTA) privacy-preserving face recognition (PPFR) systems. Our solution is robust against black-box attacking and well-established generative adversarial network (GAN) based image restoration. We analyze the potential risk in previous work, where the proposed cosine similarity computation might directly leak the protected precomputed embedding stored on the server side. We propose a Function Secret Sharing (FSS) based face embedding comparison protocol without any intermediate result leakage. In addition, we show in experiments that the proposed protocol is more efficient compared to the Secret Sharing (SS) based protocol.

Recent innovations in computer science and informatics are driving the integration of AI into modern healthcare, extending its applications to medical sectors previously reliant on human expertise. Creating robust and clinically relevant AI models requires extensive data, which can be challenging to gather, particularly when dealing with rare diseases. Data sharing among healthcare entities can address this issue, but legal, privacy, and data ownership concerns hinder such approach. To foster data sharing, in this paper we propose the GEmelli GeNerator - Real World Data (GEN-RWD) Sandbox, that provides a secure environment for data analysis without compromising sensitive medical data. This modular architecture serves as a research platform for various stakeholders, including clinical researchers, policymakers, and pharmaceutical companies. Au-thorized users submit research requests through the GUI, which are processed within the hospital, and the results can be accessed without revealing the original clinical data source. In the context of this paper we present GEN-RWD Sandbox s architecture module in charge of executing the analysis requests, the Processor. Processor s code is openly shared as the GSProcessor R package available at https://gitlab.com/benedetta.gottardelli/GSProcessor.

The resurgence of Artificial Intelligence (AI) has been accompanied by a rise in ethical issues. AI practitioners either face challenges in making ethical choices when designing AI-based systems or are not aware of such challenges in the first place. Increasing the level of awareness and understanding of the perceptions of those who develop AI systems is a critical step toward mitigating ethical issues in AI development. Motivated by these challenges, needs, and the lack of engaging approaches to address these, we developed an interactive, scenario-based ethical AI quiz. It allows AI practitioners, including software engineers who develop AI systems, to self-assess their awareness and perceptions about AI ethics. The experience of taking the quiz, and the feedback it provides, will help AI practitioners understand the gap areas, and improve their overall ethical practice in everyday development scenarios. To demonstrate these expected outcomes and the relevance of our tool, we also share a preliminary user study. The video demo can be found at https://zenodo.org/record/7601169\#.Y9xgA-xBxhF.

In this work, we provide an in-depth characterization study of the performance overhead for running Transformer models with secure multi-party computation (MPC). MPC is a cryptographic framework for protecting both the model and input data privacy in the presence of untrusted compute nodes. Our characterization study shows that Transformers introduce several performance challenges for MPC-based private machine learning inference. First, Transformers rely extensively on “softmax” functions. While softmax functions are relatively cheap in a non-private execution, softmax dominates the MPC inference runtime, consuming up to 50\% of the total inference runtime. Further investigation shows that computing the maximum, needed for providing numerical stability to softmax, is a key culprit for the increase in latency. Second, MPC relies on approximating non-linear functions that are part of the softmax computations, and the narrow dynamic ranges make optimizing softmax while maintaining accuracy quite difficult. Finally, unlike CNNs, Transformer-based NLP models use large embedding tables to convert input words into embedding vectors. Accesses to these embedding tables can disclose inputs; hence, additional obfuscation for embedding access patterns is required for guaranteeing the input privacy. One approach to hide address accesses is to convert an embedding table lookup into a matrix multiplication. However, this naive approach increases MPC inference runtime significantly. We then apply tensor-train (TT) decomposition, a lossy compression technique for representing embedding tables, and evaluate its performance on embedding lookups. We show the trade-off between performance improvements and the corresponding impact on model accuracy using detailed experiments.

Searchable encryption allows users to perform search operations on encrypted data before decrypting it first. Secret sharing is one of the most important cryptographic primitives used to design an information theoretic scheme. Nowadays cryptosys-tem designers are providing a facility to adjust the security parameters in real time to circumvent AI-enabled cyber security threats. For long term security of data which is used by various applications, proactive secret sharing allows the shares of the original secret to be dynamically adjusted during a specific interval of time. In proactive secret sharing, the updation of shares at regular intervals of time is done by the servers (participants) and not by the dealer. In this paper, we propose a novel proactive secret sharing scheme where the shares stored at servers are updated using preshared pairwise keys between servers at regular intervals of time. The direct search of words over sentences using the conjunctive search function without the generation of any index is possible using the underlying querying method.

Electronic media knowledge is unprecedently increasing in recent years. In almost all security control areas, traffic control, weather monitoring, video conferences, social media etc., videos and multimedia data analysis practices are used. As a consequence, it is necessary to retain and transmit these data, by considering the security and privacy issues. IN this research study, a new Div-Mod Stego algorithm is combined with the Multi-Secret Sharing method along with temporary frame reordering and Genetic algorithm to implement high-end security in the process of video sharing. The qualitative and quantitative analysis has also been carried out to compare the performance of the proposed model with the other existing models. A computer analysis shows that the proposed solution would satisfy the requirements of the real-time application.

At present people can easily share multimedia information on Internet, which leads to serious data security issues. Especially in medical, military and financial fields, images always contain a lot of sensitive information. To safely transmit images among people, many secret image sharing methods are proposed. However, the existing methods can not solve the problems of pixel expansion and high computational complexity of shadow images at the same time. In this paper, we propose an image sharing method by combining sharing matrix and variational hyperprior network, to reduce the pixel expansion and computational complexity of secret image sharing methods. The method uses the variational hyperprior network to encode images. It introduces the hyperprior to effectively catch spatial dependencies in the latent representation, which can compress image with high efficiency. The experimental results show that our method has low computational complexity and high security performance compared with the state-of-the-art approaches. In addition, the proposed method can effectively reduce the pixel expansion when using the sharing matrix to generate shadow images.

Quantum secret sharing (QSS) is a cryptography technique relying on the transmission and manipulation of quantum states to distribute secret information across multiple participants securely. However, quantum systems are susceptible to various types of noise that can compromise their security and reliability. Therefore, it is essential to analyze the influence of noise on QSS to ensure their effectiveness and practicality in real-world quantum communication. This paper studies the impact of various noisy environments on multi-dimensional QSS. Using quantum fidelity, we examine the influence of four noise models: d-phase-flip(dpf), dit-flip(df), amplitude damping(ad), and depolarizing(d). It has been discovered that the fidelity declines with an increase in the noise parameter. Furthermore, the results demonstrate that the efficiency of the QSS protocol differs significantly across distinct noise models.