Functional Encryption Research Articles

Privacy-Preserving Machine Learning Using Functional Encryption: Opportunities and Challenges

Privacy-Preserving Machine Learning Using Functional Encryption: Opportunities and Challenges

IRepair or I Repair? A Dialectical Process Analysis of Control Enactment in the iPhone Repair Aftermarket

We study how Apple and independent repair service providers used different physical, regulatory, and digital instruments to influence each other’s abilities to control the repair aftermarket of the Apple iPhone between 2007 and 2020. We show how the emergence of digital instruments for enacting control, made possible through emerging functionality for tethering, encryption, and temporary binding implemented in the iPhone itself, was shaped by and shaped the actions of Apple and the independent repair service providers, and led to a dominant tension between encrypted authorization and inscription of control through Apple and collective legal action by independent repair service providers. Our analysis provides a more nuanced understanding of control enactment by highlighting the implications of using different mediums for exercising control, and we provide a new way to understand the dialectics involved in enacting control in digital product aftermarkets. Our study provides insights that can inform the regulation of digital product aftermarkets—in particular, the ongoing debate about rights-to-repair legislation.

АРХІТЕКТУРА ПРОГРАМНОЇ СИСТЕМИ ДЛЯ ВИРІШЕННЯ ЗАДАЧІ КЛАСИФІКАЦІЇ НА ОСНОВІ ПРИВАТНИХ ДАНИХ

Data analysis and artificial intelligence systems are becoming widely used in various spheres of human life. This is confirmed by more typical cases of their use, in particular, the selection of recommendations for the user in e-commerce, the detection of spam in e-mail services, and the moderation of user comments; as well as cases of personal use of such tools (for example, chatbots ChatGPT, Google Bard, Microsoft Copilot have appeared and gained significant popularity in the last two years). One of the key elements of such systems is data, which is necessary for training and testing software systems of intelligent data analysis. A significant amount of diverse data contributes to the construction of a software system with high accuracy. Considering this, the task of choosing and preparation of datasets that can be used in the construction of such systems is important. One of the difficulties in this task is the presence of private information in the datasets, which limits their use for systems of intelligent data analysis. The paper is devoted to the development of the software system architecture for solving the classification problem based on private data. The existing methods and architectural approaches for privacy-preserving in machine learning were considered. The architecture of the software system was proposed, the characteristic feature of which is the protection of private datasets by using of functional encryption, which allows to increase the number of datasets for training publicly available data analysis and artificial intelligence systems. The proposed architecture of the software system is based on the client-server architecture and functional encryption. The components are a classifier, a generator of encryption keys, and modules of functional encryption and decryption. Prospects for further research were discussed.

Securing Medical Images Using Chaotic Map Encryption and LSB Steganography

Securing Medical Images Using Chaotic Map Encryption and LSB Steganography

Highly Thermally Conductive Aramid Nanofiber Composite Films with Synchronous Visible/Infrared Camouflages and Information Encryption

AbstractThe development of highly thermally conductive composites that combine visible light/infrared camouflage and information encryption has been endowed with great significance in facilitating the application of 5G communication technology in military fields. This work uses aramid nanofibers (ANF) as the matrix, hetero‐structured silver nanowires@boron nitride nanosheets (AgNWs@BNNS) prepared by in situ growth as fillers, which are combined to fabricate sandwich structured thermally conductive and electrically insulating (BNNS/ANF)‐(AgNWs@BNNS)‐(BNNS/ANF) (denoted as BAB) composite films by “filtration self‐assembly, air spraying, and hot‐pressing” method. When the mass ratio of AgNWs@BNNS to BNNS is 1 : 1 and the total mass fraction is 50 wt %, BAB composite film has the maximum in‐plane thermal conductivity coefficient (λ∥ of 10.36 W/(m ⋅ K)), excellent electrical insulation (breakdown strength and volume resistivity of 41.5 kV/mm and 1.21×1015 Ω ⋅ cm, respectively) and mechanical properties (tensile strength of 170.9 MPa). 50 wt % BAB composite film could efficiently reduce the equilibrium temperature of the central processing unit (CPU) working at full power, resulting in 7.0 °C lower than that of the CPU solely integrated with ANF directly. In addition, BAB composite film boasts adaptive visible light/infrared dual camouflage properties on cement roads and jungle environments, as well as the function of fast encryption of QR code information within 24 seconds.

Highly Thermally Conductive Aramid Nanofiber Composite Films with Synchronous Visible/Infrared Camouflages and Information Encryption.

The development of highly thermally conductive composites that combine visible light/infrared camouflage and information encryption has been endowed with great significance in facilitating the application of 5G communication technology in military fields. This work uses aramid nanofibers (ANF) as the matrix, hetero-structured silver nanowires@boron nitride nanosheets (AgNWs@BNNS) prepared by in situ growth as fillers, which are combined to fabricate sandwich structured thermally conductive and electrically insulating (BNNS/ANF)-(AgNWs@BNNS)-(BNNS/ANF) (denoted as BAB) composite films by "filtration self-assembly, air spraying, and hot-pressing" method. When the mass ratio of AgNWs@BNNS to BNNS is 1 : 1 and the total mass fraction is 50 wt %, BAB composite film has the maximum in-plane thermal conductivity coefficient (λ∥ of 10.36 W/(m ⋅ K)), excellent electrical insulation (breakdown strength and volume resistivity of 41.5 kV/mm and 1.21×1015 Ω ⋅ cm, respectively) and mechanical properties (tensile strength of 170.9 MPa). 50 wt % BAB composite film could efficiently reduce the equilibrium temperature of the central processing unit (CPU) working at full power, resulting in 7.0 °C lower than that of the CPU solely integrated with ANF directly. In addition, BAB composite film boasts adaptive visible light/infrared dual camouflage properties on cement roads and jungle environments, as well as the function of fast encryption of QR code information within 24 seconds.

An Efficient Privacy Protection Mechanism for Blockchain-Based Federated Learning System in UAV-MEC Networks.

The widespread use of UAVs in smart cities for tasks like traffic monitoring and environmental data collection creates significant privacy and security concerns due to the transmission of sensitive data. Traditional UAV-MEC systems with centralized data processing expose this data to risks like breaches and manipulation, potentially hindering the adoption of these valuable technologies. To address this critical challenge, we propose UBFL, a novel privacy-preserving federated learning mechanism that integrates blockchain technology for secure and efficient data sharing. Unlike traditional methods relying on differential privacy (DP), UBFL employs an adaptive nonlinear encryption function to safeguard the privacy of UAV model updates while maintaining data integrity and accuracy. This innovative approach enables rapid convergence, allowing the base station to efficiently identify and filter out severely compromised UAVs attempting to inject malicious data. Additionally, UBFL incorporates the Random Cut Forest (RCF) anomaly detection algorithm to actively identify and mitigate poisoning data attacks. Extensive comparative experiments on benchmark datasets CIFAR10 and Mnist demonstrably showcase UBFL's effectiveness. Compared to DP-based methods, UBFL achieves accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%) in privacy preservation while maintaining superior accuracy. Notably, under data pollution scenarios with varying attack sample rates (10%, 20%, and 30%), UBFL exhibits exceptional resilience, highlighting its robust capabilities in securing UAV gradients within MEC environments.

Access control mechanism in distributed smart power plants based on blockchain and ciphertext updatable functional encryption

Access control mechanism in distributed smart power plants based on blockchain and ciphertext updatable functional encryption

A Robust Zero-Watermarking Scheme in Spatial Domain by Achieving Features Similar to Frequency Domain

In recent years, there has been a substantial surge in the application of image watermarking, which has evolved into an essential tool for identifying multimedia material, ensuring security, and protecting copyright. Singular value decomposition (SVD) and discrete cosine transform (DCT) are widely utilized in digital image watermarking despite the considerable computational burden they involve. By combining block-based direct current (DC) values with matrix norm, this research article presents a novel, robust zero-watermarking approach. It generates a zero-watermark without attempting to modify the contents of the image. The image is partitioned into non-overlapping blocks, and DC values are computed without applying DCT. This sub-image is further partitioned into non-overlapping blocks, and the maximum singular value of each block is calculated by matrix norm instead of SVD to obtain the binary feature matrix. A piecewise linear chaotic map encryption technique is utilized to improve the security of the watermark image. After that, the feature image is created via XOR procedure between the encrypted watermark image and the binary feature matrix. The proposed scheme is tested using a variety of distortion attacks including noise, filter, geometric, and compression attacks. It is also compared with the other relevant image watermarking methods and outperformed them in most cases.

Partially-hiding functional encryption for degree-2 polynomials with fine-grained access control

Partially-hiding functional encryption for degree-2 polynomials with fine-grained access control

Single-actuated and fully recyclable phase change materials enabled multiple thermochromism toward information storage and encryption

The stimulus-responsive chromic materials under the single-triggered condition are hard to deal with the advanced forgery or tampering technologies due to its simple color variation. Herein, a novel multiple thermochromism materials (G-LPCMs) was synthesizing through structuring a gel factor as skeleton to adsorb intrinsic thermochromic component (TC) and thermochromic microspheres (TCM) in a simple way. The cooperation of TC and TCM can endow the G-LPCMs with rich color variations from blue to white and ultimately to pink at different temperature ranges, realizing multiple color variations in one material. Capitalizing on this rich color variation characteristic, a digital encryption system can be designed only by using its color change under single thermal-triggered condition. The devised cryptographic system can reversibly achieve the functions of information storage, information erasure and information reorganization during the cyclic heating–cooling process. Moreover, the synthetic G-LPCMs can be recycled only by simple heating, those chemical structure, crystal structure, and information encryption function can basically remain unchanged after multiple thermal cycles, greatly enhancing their service life for the security system. This work offers a new design for thermochromic PCMs that have the rich color variation characteristic in one material under single stimulus.

FEDL: Confidential Deep Learning for Autonomous Driving in VANETs Based on Functional Encryption

FEDL: Confidential Deep Learning for Autonomous Driving in VANETs Based on Functional Encryption

Securely Cloud Data Storage and Sharing

With the increasing adoption of cloud computing, data storage and sharing have be-come integral parts of our digital lives. However, ensuring the security and privacy of data stored in the cloud remains a significant challenge. This paper proposes a novel approach for securely storing and sharing data in the cloud, addressing the vulnerabilities associated with traditional cloud storage models. The proposed approach utilizes advanced cryptographic techniques, including hyperchaotic encryption and hash functions, to protect the confidentiality and integrity of data stored in the cloud. The hyperchaotic encryption algorithm provides a high level of security by introducing chaos-based dynamics into the encryption process, making it resistant to various attacks. Additionally, the hash function ensures the integrity of data by generating unique identifiers for each file stored in the cloud. To enhance data sharing security, the proposed approach employs access control mechanisms and user authentication protocols. Access control rules are enforced to restrict unauthorized access to data, while user authentication ensures that only legitimate users can access and modify the shared data.

Functional encryption implementation to protect storage data in the cloud

Functional encryption implementation to protect storage data in the cloud

CP-IPFE: Ciphertext-Policy Based Inner Product Functional Encryption

CP-IPFE: Ciphertext-Policy Based Inner Product Functional Encryption

Functional encryption implementation to protect storage data in the cloud

Functional encryption implementation to protect storage data in the cloud

Privacy‐Enhanced Data Sharing Systems from Hierarchical ID‐Based Puncturable Functional Encryption with Inner Product Predicates

The emergence of cloud computing enables users to upload data to remote clouds and compute them. This drastically reduces computing and storage costs for users. Considering secure computing for multilevel users in enterprises, the notion of hierarchical identity‐based inner product functional encryption (HIB‐IPFE) is proposed. In this cryptosystem, a sender can encrypt a vector into a ciphertext with a hierarchical identity, while a receiver who possesses a secret key corresponding to the same hierarchical identity and a vector can decrypt the ciphertext and obtain the inner product . However, HIB‐IPFE is not sufficient to capture flexible data sharing and forward security. In this study, we present a notion of hierarchical identity‐based puncturable HIBP‐IPFE. Furthermore, we present a formal definition and security model of HIBP‐IPFE to guarantee data confidentiality and receiver anonymity. Compared with HIB‐IPFE, our proposed scheme enables users to puncture keys on specific tags ensuring that the punctured keys cannot be used to decrypt the ciphertexts associated with those tags. The proposed scheme is provably secure under d‐DBDHE assumption in the standard model. The experimental results indicate that our scheme is more practical in cloud computing, with superior functionality.

DRcipher: A pseudo-random dynamic round lightweight block cipher

The Internet of Things (IoT) has gained popularity in various fields, including components such as embedded devices and wireless sensors. However, ensuring the security of data transmission from these devices is of critical importance. In light of these challenges, a novel lightweight encryption method called DRcipher is proposed in this paper for resource-constrained IoT devices. DRcipher has a 64-bit block size and supports either 96-bit or 128-bit key sizes. In order to improve the security, DRcipher employs a pseudo-random number of encryption rounds determined by the primary key. DRcipher adopts the structure of Generalized Feistel Network (GFN) with 4 branches, and its round functions consist of F-function, FF-function and RP permutation components. In particular, there is a negative feedback mechanism between the FF-function and the overall round of encryption functions. In addition, DRcipher is synthesised using Synopsys Design Compiler version A-2007.12-SP1 and the UMCL18G212T3 standard cell library. DRcipher-96 has an area footprint of 1546 Gate Equivalents (GE), while DRcipher-128 has a slightly larger area footprint of 1646 GE. Moreover, a comprehensive security analysis shows that the proposed DRcipher ensures high-level security redundancy against differential cryptanalysis, linear cryptanalysis, and so on.

Fully collusion resistant trace-and-revoke functional encryption for arbitrary identities

Functional Encryption (FE) has been extensively studied in the recent years, mainly focusing on the feasibility of constructing FE for general functionalities, as well as some realizations for restricted functionalities of practical interest, such as inner-product. However, little consideration has been given to the issue of key leakage on FE. The property of FE that allows multiple users to obtain the same functional keys from the holder of the master secret key raises an important problem: if some users leak their keys or collude to create a pirate decoder, how can we identify at least one of those users, given some information about the compromised keys or the pirate decoder? Moreover, how do we disable the decryption capabilities of those users (i.e. traitors)?Two recent works have offered potential solutions to the above traitor scenario. However, the two solutions satisfy weaker notions of security and traceability, can only tolerate bounded collusions (i.e., there is an a priori bound on the number of keys the pirate decoder obtains), or can only handle a polynomially large universe of possible identities. In this paper, we study trace-and-revoke mechanism on FE and provide the first construction of trace-and-revoke FE that supports arbitrary identities, is both fully collusion resistant and fully anonymous. Our construction relies on a generic transformation from revocable predicate functional encryption with broadcast (RPFE with broadcast, which is an extension of revocable predicate encryption with broadcast proposed by Kim and J. Wu at ASIACRYPT'2020) to trace-and-revoke FE. Since this construction admits a generic construction of trace-and-revoke inner-product FE (IPFE), we instantiate the trace-and-revoke IPFE from the well-studied Learning with Errors (LWE). This is achieved by proposing a new LWE-based attribute-based IPFE (ABIPFE) scheme to instantiate RPFE with broadcast.

Designing An Encryption Function for Chaotic-based Model with Bifurcation Shifting in Security Communicate

Designing An Encryption Function for Chaotic-based Model with Bifurcation Shifting in Security Communicate