Distributed Simulation of Encrypted Dynamics via Functional Mockup Units

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

Somewhat Homomorphic Encryption (SHE) schemes allow to carry out operations on data in the cipher domain. In a cloud computing scenario, personal information can be processed secretly, inferring a high level of confidentiality. Most practical Somewhat Homomorphic Encryption (SHE) schemes require the implementation of fast polynomial arithmetic, that is why hardware accelerators usually target the FFT/NTT algorithm. This paper proposes a co-design hardware/software approach to accelerate SHE using Karatsuba algorithm. Depending on the needs, Karatsuba algorithm allows to implement additional computations to the hardware in order to reduce software computation time. Our accelerator is designed to speed up arithmetic on degree 2560 polynomials with 125 bits coefficients. We provide 3 different approaches: An area efficient design, a balanced design, and a performance-oriented design. Our accelerator performs a polynomial multiplication in respectively 2.46 ms, 1.70 ms and 1.24 ms, and a relinearization operation in 2.28 ms, 1.53 ms and 1.1 ms, while a functionally equivalent design using the FFT [1] performs the multiplication in 1.96 ms and the relinearization in 4.79 ms for hardware resources consumption equivalent to the balanced design.

Healthcare data has risen as a top target for cyberattacks due to the rich amount of sensitive patient information. This negatively affects the potential of advanced analytics and collaborative research in healthcare. Homomorphic encryption (HE) has emerged as a promising technology for securing sensitive healthcare data while enabling computations on encrypted information. This paper conducts a background survey of HE and its various types. It discusses Partially Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), Fully Homomorphic Encryption (FHE) and Fully Leveled Homomorphic Encryption (FLHE). A critical analysis of these encryption paradigms’ theoretical foundations, implementation schemes, and practical applications in healthcare contexts is presented. The survey encompasses diverse healthcare domains. It demonstrates HE’s versatility in securing electronic health records (EHRs), enabling privacy-preserving genomic data analysis, protecting medical imaging, facilitating privacy-preserving machine learning (ML), supporting secure federated learning, ensuring confidentiality in clinical trials, and enhancing remote monitoring and telehealth services. A comprehensive examination of potential vulnerabilities in HE systems is conducted. The research systematically investigates various attack vectors, including side-channel attacks, key recovery attacks, chosen plaintext attacks (CPA), chosen ciphertext attacks (CCA), known plaintext attacks (KPA), fault injection attacks (FIA), and lattice attacks. A detailed analysis of potential defense mechanisms and mitigation strategies is provided for each identified threat. The analysis underscores the importance of HE for long-term security and sustainability in healthcare systems.

Deep learning (DL) has shown outstanding performances in many fields, including remote sensing (RS). DL is turning into an essential tool for the RS research community. Recently, many cloud platforms have been developed to provide access to large-scale computing capacity, consequently permitting the usage of DL architectures as a service. However, this opened the door to new challenges associated with the privacy and security of data. The RS data used to train the DL algorithms have several privacy requirements. Some of them need a high level of confidentiality, such as satellite images related to public security with high spatial resolutions. Moreover, satellite images are usually protected by copyright, and the owner may strictly refuse to share them. Therefore, privacy-preserving deep learning (PPDL) techniques are a possible solution to this problem. PPDL enables training DL on encrypted data without revealing the original plaintext. This study proposes a hybrid PPDL approach for object classification for very-high-resolution satellite images. The proposed encryption scheme combines Paillier homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). This combination aims to enhance the encryption of satellite images while ensuring a good runtime and high object classification accuracy. The method proposed to encrypt images is maintained through the public keys of PHE and SHE. Experiments were conducted on real-world high-resolution satellite images acquired using the SPOT6 and SPOT7 satellites. Four different CNN architectures were considered, namely ResNet50, InceptionV3, DenseNet169, and MobileNetV2. The results showed that the loss in classification accuracy after applying the proposed encryption algorithm ranges from 2% to 3.5%, with the best validation accuracy on the encrypted dataset reaching 92%.

Co-simulation is a means to combine and leverage the strengths of different modeling tools, environments and formalisms and has been applied successfully in various domains. The Functional Mock-Up Interface (FMI) is the most commonly used standard for co-simulation. In this paper we extend UniFMU, a tool that allows users to build Functional Mock-Up Units (FMUs) in virtually any programming language, to support execution within Docker. As a result the generated FMUs can be distributed in an environment containing all runtime dependencies. To describe the process of creating dockerized FMUs using UniFMU, we show how to model and co-simulate a robotic arm and a controller using two Python-based FMUs.

The Functional Mock-up Interface standard allows to generate stand-alone sub-systems which can be simulated and verified individually. In this paper we present a design of a model aggregation which allows to simulate several Functional Mock-up Units as a coupled model. The formulation is based on Assimulo as a numerical integration environment. Assimulo problem classes are extended to a class for aggregated problems which collects information provided by the Functional Mockup Units through the tool PyFMI together with Python based problem classes defined by Assimulo. This allows to set-up test environments of complex models composed of several sub-systems.

We present a computationally secure MPC protocol for threshold adversaries which is parametrized by a value L. When L = 2 we obtain a classical form of MPC protocol in which interaction is required for multiplications, as L increases interaction is reduced, in that one requires interaction only after computing a higher degree function. When L approaches infinity one obtains the FHE based protocol of Gentry, which requires no interaction. Thus one can trade communication for computation in a simple way. Our protocol is based on an interactive protocol for “bootstrapping” a somewhat homomorphic encryption (SHE) scheme. The key contribution is that our presented protocol is highly communication efficient enabling us to obtain reduced communication when compared to traditional MPC protocols for relatively small values of L.

Fully Homomorphic Encryption (FHE) is the holy grail of encrypted communications. It opens the door to several advanced functionalities to overcome the security and trust issues of the IT world. After 2009, once Craig Gentry had shown that FHE could be achieved, a study in this field boomed, and significant improvement was made in identifying more efficient and realistic programs. FHE is primitive cryptography that enables arbitrary functions to be calculated via encrypted data. These systems are applicable in different ways since they permit users to encrypt their private information securely while still outsourcing the processing of protected data without fearing disclosing the real data. In 2012, LTV12 presented the first multi-key FHE system and demonstrated the possibility of using multi-key systems in somewhat homomorphic encryption (SHE). Like in the one key context, there have been many advances in the field, but no effort has been made to develop the multi-key methods. This paper presents a discussion of FHE and MKFHE with a specific focus on the current techniques and three implementations, comprising the first in the multi-key setups, to the extent of our understanding.

Outsourcing encrypted data to the cloud demands efficient and confidentiality-preserving search techniques, yet existing Searchable Symmetric Encryption (SSE) schemes suffer from severe access and search pattern leakage, and the problem is more intractable in multi-data-owner scenarios where cross-dataset privacy-preserving search remains unsolved. This paper proposes PSSE-MDO (Privacy-Preserving SSE with Pattern Obfuscation for Multiple Data Owners), the first SSE scheme that achieves robust access and search pattern hiding for multi-data-owner settings with constant client-side overhead. Our core innovation is a cryptographic protocol enabling users to issue a single constant-sized search token (trapdoor) for searching across all data owners' encrypted indexes. The scheme integrates Inner-Product Predicate Encryption (IPE) and Somewhat Homomorphic Encryption (SHE) to probabilistically obfuscate search results for each query. A key experimental result shows that the client-side cost approximately 120 ms for search token generation—remains constant regardless of the number of data owners, in contrast to existing pattern-hiding schemes with linearly scaling costs. This validates PSSE-MDO's practicality for real-world large-scale collaborative cloud systems, marking a significant step toward practical privacy-preserving search in federated cloud environments.

With the increasingly powerful and extensive deployment of edge devices, edge/fog computing enables customers to manage and analyze data locally, and extends computing power and data analysis applications to network edges. Meanwhile, as the next generation of the power grid, the smart grid can achieve the goal of efficiency, economy, security, reliability, use safety and environmental friendliness for the power grid. However, privacy and secure issues in fog-based smart grid communications are challenging. Without proper protection, customers’ privacy will be readily violated. This article presents a smart and practical Privacy-preserving Data Aggregation (PDA) scheme with smart pricing and packing method for fog-based smart grids, which achieves diversified tariffs, multifunctional statistics and efficiency. Especially, we first propose a smart PDA scheme with Smart Pricing (PDA-SP). With PDA-SP, the Control Center (CC) can compute more complex and higher-order aggregation statistics to provide various services, provide diversiform pricing strategies and choose a double-winning strategy. Subsequently, we put forward a practical PDA scheme with Packing Method (PDA-PM), which is able to reduce the size of encrypted data and improve performance in performing various secure computations. Moreover, we extend our original packing method and present a more useful packing method, which can handle general vectors with large entries. The security analysis shows that our proposed scheme is secure against many threats. The performance evaluation reveals that the computation and communication overheads of our proposed scheme are effectively reduced by employing the Somewhat Homomorphic Encryption (SHE), and our packing method can further significantly reduce these overheads.

A co-simulation modelling approach for the assessment of a ventilated double-skin complex fenestration system coupled with a compact fan-coil unit

This paper describes the results of an evaluation of vehicle power network modeling using Functional Mock-up Interface (FMI). FMI, developed by the Modelica Association, is a tool-independent standard for a model interface. This study compared simulation results between the vehicle power network model with Functional Mock-up Units (FMUs) connected and the model without FMUs. Conversion adaptors were inserted to generate and connect each FMU because it was necessary to convert physical connections from acausal connections to causal inputs and outputs. Using FMI for Model Exchange, it was confirmed that the results for both simulations showed good matches, without any time delays although algebraic loops which would often occur in electrical circuit modeling were included. This method possesses significant potential to reduce the validation effort using models created by other departments or suppliers.

Reconciling interoperability with efficient Verification and Validation within open source simulation environments

Different causes have contributed to the shift of paradigm in the automation field from centralized to modular and distributable. Software and hardware of Programmable Logic Controllers (PLCs) have evolved to be synchronized with the demands nowadays and new standards are trying to follow this trend. One of them is the IEC 61499 standard, which is intended for modelling distributed industrial solutions with a vendor-independent format. This shifting is also bringing new challenges and this paper focuses on the virtual commissioning of plants, which can become a harder task when the system is distributed and involves different interconnected modules. A major problem when doing a virtual commissioning is the coupling between the different physical systems and controlling tools since many vendors still offer closed integrated solutions. Functional Mockup Interface (FMI) is a standard for a cosimulation interface which intends to fill the gaps between different modelling tools, by packaging the models into Functional Mockup Unit (FMU) and allowing a co-simulation master algorithm to have access to the inside model through the proposed interface. This paper presents a mapping between IEC 61499 models and the FMI standard and an implementation of a tool that can export IEC 61499 models into FMUs, which would allow the co-simulation of physical plants and the PLCs software that controls it. An experiment is shown where a simple Proportional-Integral-Derivative (PID) controller of a tank system is exported as an FMU and co-simulated together with a tank system modelled using OpenModelica.

In this paper, we describe how we generate Functional Mock-up Units (FMUs) for the automation block language Bloqqi. This allows Bloqqi control programs to be tested with simulations of the physical processes they control. The physical process can be specified in any tool that supports the Functional Mockup-Interface (FMI) standard. For example, we have successfully run Bloqqi programs together with Modelica models exported as FMUs. Bloqqi programs execute at discrete times, and we describe how this is handled in the implementation of the DoStep function, specified in the standard.