Functional Encryption Research Articles

Enhancing UAV‐HetNet security through functional encryption framework

SummaryIn the current landscape, the rapid expansion of the internet has brought about a corresponding surge in the number of data consumers. As user volume and diversity have escalated, the shift from conventional, uniform networks to Heterogeneous Networks (HetNets) has emerged. HetNets are designed with a primary objective: enhancing Quality of Service (QoS) standards for users. In the context of HetNets facilitated by Unmanned Aerial Vehicles (UAVs), a substantial influx of users and devices is observed. Within this multifaceted environment, the potential for malicious intruder nodes to efficiently execute and propagate harmful actions across the network is a distinct concern. Consequently, the entirety of network communication becomes susceptible to a multitude of security threats. To address these vulnerabilities and safeguard communication, the Functional Encryption (FE) technique is employed. FE empowers the protection of data against intrusion attacks. This paper presents a comprehensive methodology for implementing FE within UAV‐integrated HetNets, executed in two sequential phases. The initial phase secures communication between User Equipment (UE) and Micro Base Station (MBS), followed by the second phase, which focuses on securing communication among MBS and UAV. The viability of the proposed approach is substantiated through validation using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The validation process involves the development of High‐Level Protocol Specification Language (HLPSL) codes. The successful security validation outcome underscores the capacity of the proposed methodology to provide the intended security measures and robustness to the network environment.

Secureimagesec: A privacy-preserving framework for outsourced picture representation with content-based image retrieval

Content-Based Image Retrieval (CBIR) uses complicated algorithms to analyze visual attributes and retrieve relevant photos from large databases. CBIR is essential to a privacy-preserving feature extraction and protection method for outsourced picture representation. SecureImageSec combines essential methods with the system’s key entities to ensure secure, private and protected image feature processing during outsourcing. For a system to be implemented effectively, these techniques must be seamlessly integrated across critical entities, such as the client, the cloud server that is being outsourced, the component that protects secure features, the component that maintains privacy in communication, access control, and authorization, and the integration and system evaluation. The client entity initiates outsourcing using advanced encryption techniques to protect privacy. SecureImageSec protects outsourced data by using cutting-edge technologies like Fully Homomorphic Encryption (FHE) and Secure Multi-Party Computation (SMPC). Cloud servers hold secure feature protection entities and protect outsourced features’ privacy and security. SecureImageSec uses AES and FPE to protect data format. SecureImageSec’s cloud-outsourced privacy-preserving communication uses SSL/TLS and QKD to protect data transmission. Attribute-Based Encryption (ABE) and Functional Encryption (FE) in SecureImageSec limit access to outsourced features based on user attributes and allow fine-grained access control over decrypted data. SecureImageSec’s Information Leakage Rate (ILR) of 0.02 for a 1000-feature dataset shows its efficacy. SecureImageSec also achieves 4.5 bits of entropy, ensuring the encrypted feature set’s muscular cryptographic strength and randomness. Finally, SecureImageSec provides secure and private feature extraction and protection, including CBIR capabilities, for picture representation outsourcing.

A lattice-based data sharing functional encryption scheme with HRA security for IoT

In the dynamic and resource-constrained landscape of IoT, securing sensitive data access is paramount. This paper introduces an innovative access control scheme (ACPRE) blending conditional attribute-based proxy re-encryption with inner product encryption, tailored for IoT security needs. ACPRE integrates attribute-based conditions, expands plaintext space, and restricts proxy privileges, bolstering access control. Leveraging inner product encryption, it confines decrypted information to inner product values, ensuring data confidentiality. By limiting proxies’ transformation abilities, ACPRE prevents unauthorized data alterations. It offers a secure, flexible solution for data access management, allowing only entities with specific attributes to utilize inner product values. ACPRE’s lattice-based construction enables resistance against quantum attacks and achieves honestly re-encryption attacks security, fostering robust, privacy-aware IoT ecosystems. Detailed security proofs and experimental validation underscore its efficacy.

Multi-key and Multi-input Predicate Encryption (for Conjunctions) from Learning with Errors

We put forward two natural generalizations of predicate encryption (PE), dubbed multi-key and multi-input PE. More in details, our contributions are threefold.Definitions. We formalize security of multi-key PE and multi-input PE following the standard indistinguishability paradigm, and modeling security both against malicious senders (i.e., corruption of encryption keys) and malicious receivers (i.e., collusions).Constructions. We construct adaptively secure multi-key and multi-input PE supporting the conjunction of poly-many arbitrary single-input predicates, assuming the sub-exponential hardness of the learning with errors (LWE) problem.Applications. We show that multi-key and multi-input PE for expressive enough predicates suffices for interesting cryptographic applications, including non-interactive multi-party computation (NI-MPC) and matchmaking encryption (ME). In particular, plugging in our constructions of multi-key and multi-input PE, under the sub-exponential LWE assumption, we obtain the first ME supporting arbitrary policies with unbounded collusions, as well as robust (resp. non-robust) NI-MPC for so-called all-or-nothing functions satisfying a non-trivial notion of reusability and supporting a constant (resp. polynomial) number of parties. Prior to our work, both of these applications required much heavier tools such as indistinguishability obfuscation or compact functional encryption.

An Analysis of Blockchain-Based IoT Sensor Network Distributed Denial of Service Attacks.

The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks.

Depth-Optimized Quantum Circuits for ASCON: AEAD and HASH

Quantum computing advancements pose security challenges for cryptography. Specifically, Grover’s search algorithm affects the reduction in the search complexity of symmetric-key encryption and hash functions. Recent efforts have been made to estimate the complexity of Grover’s search and evaluate post-quantum security. In this paper, we propose a depth-optimized quantum circuit implementation for ASCON, including both symmetric-key encryption and hashing algorithms, as a part of the lightweight cryptography standardization by NIST (National Institute of Standards and Technology). As far as we know, this is the first implementation of a quantum circuit for the ASCON AEAD (Authenticated Encryption with Associated Data) scheme, which is a symmetric-key algorithm. Also, our quantum circuit implementation of the ASCON-HASH achieves a reduction of more than 88.9% in the Toffoli depth and more than 80.5% in the full depth compared to the previous work. As per our understanding, the most effective strategy against Grover’s search involves minimizing the depth of the quantum circuit for the target cipher. We showcase the optimal Grover’s search cost for ASCON and introduce a proposed quantum circuit optimized for depth. Furthermore, we utilize the estimated cost to evaluate post-quantum security strength of ASCON, employing the relevant evaluation criteria and the latest advancements in research.

Analysis of the Dynamics of a Cubic Nonlinear Five-Dimensional Memristive Chaotic System and the Study of Reduced-Dimensional Synchronous Masking

To improve the complexity of the chaotic system and achieve the effective transmission of image information, in this paper, a five-dimensional memristive chaotic system with cubic nonlinear terms is constructed, which has four pairs of symmetric coordinates. First, the cubic nonlinear memristive chaotic system is analyzed using the Lyapunov exponential map, bifurcation map, and attractor phase diagram. The experimental results show that under four pairs of symmetric coordinates, the system exists not only parameter-dependent symmetric rotational coexisting attractor and transient chaotic phenomena but also exists super-multistationary with alternating chaotic cycles dependent on the initial value of the memristor. Then, it is proposed to add a constant term to the linear state variable to explore the effect of the offset increment of the linear state variable on the system in four pairs of symmetric coordinates, while circuit simulation of the five-dimensional chaotic system is carried out using Simulink to verify its existence and realisability. Finally, the synchronization of the dimensionality reduction system and the confidential transmission of the image are achieved, using the control voltage of the system to replace the internal state variables of the memristor to achieve the one-dimensional reduction process, and an adaptive synchronization controller is designed to synchronize the system before and after the dimensionality reduction. Based on the above, an image to be transmitted is modulated into a one-dimensional array and then subjected to the fractional and cyclic operations and combined with the linear encryption and decryption functions and the chaotic masking technique, the simple encryption and decryption of the image processes are realized.

Personalized and privacy-preserving federated graph neural network

High-performance GNN obtains dependencies within a graph by capturing the mechanism of message passing and aggregation between neighboring nodes in the graph, and successfully updates node embeddings. However, in practical applications, the inherent model structure of the graph is highly susceptible to privacy attacks, and the heterogeneity of external data can lead to a decrease in model performance. Motivated by this challenge, this work proposes a novel framework called Personalized Federated Graph Neural Network for Privacy-Preserving (PFGNN). Specifically, firstly, this work introduces a graph similarity strategy. Based on the principle that clients with similar features exhibit stronger homophily, this work divides all participating clients into multiple clusters for collaborative training. Furthermore, within each group, this work employs an attention mechanism to design a federated aggregation weighting scheme. This scheme is used to construct a global model on the server, which helps mitigate the difficulty of model generalization resulting from data heterogeneity collected from different clients. Lastly, to ensure the privacy of model parameters during the training process and prevent malicious adversaries from stealing them, this work implements privacy-enhancing technology by introducing an optimized function-hiding multi-input function encryption scheme. This ensures the security of both model data and user privacy. Experiments on real datasets show that our scheme outperforms FedAvg in accuracy, and the communication overhead is linearly related to the number of clients. Through this framework, PFGNN can handle all kinds of non-Euclidean structured data, multiple clients collaborate to train high-quality and highly secure global models. This work provides the foundation for designing efficient and privacy-preserving personalized federated graph neural networks.

IS-DGM: an improved steganography method based on a deep generative model and hyper logistic map encryption via social media networks

IS-DGM: an improved steganography method based on a deep generative model and hyper logistic map encryption via social media networks

Fluorescent hydrogel with high toughness response based on lanthanide Metals: Material Adhesion, multicolor Modulation, information encryption

Natural polymer-based luminescent materials (NPLMs) are widely used in various fields of science and technology during the high-tech era. This study describes the formation of polyacrylamide (PAM) by thermally initiated free radical in situ polymerization of acrylamide monomer. The resulting hydrogel is physically entangled and chemically crosslinked with hypromellose (HPMC) to create a three-dimensional network structure. Lanthanide organic compatibilizers (Ln-L) are then introduced as luminescent units to produce fluorescent hydrogel materials. The study found that the tensile strength of PAM/HPMC/Ln (0/6 Eu/Tb) hydrogel increased by 230.53 % from 11.76 to 27.11 MPa. Similarly, the compressive strength increased by 208.30 % from 16.14 to 33.62 MPa. Additionally, by adjusting the molar ratio of lanthanide metals, hydrogels can achieve colorful fluorescence modulation. Moreover, an information encryption function was implemented using a Python design program in combination with the fluorescent properties of hydrogels.

A Study on the Navigating Cybersecurity Challenges in the Digital-era

In today's era Navigating cybersecurity concerns is a top priority for individuals, corporations, and governments in today's digital landscape. This in-depth research explores the complex realm of cybersecurity, looking at the new dangers and the methods used to successfully counter them. The swift growth of digital technologies, encompassing cloud computing and the widespread use of Internet of Things devices, has greatly expanded the attack surface, revealing weaknesses that malevolent actors can promptly take advantage of. Gaining an understanding of these weaknesses is essential to creating strong defenses. The present study centers on pivotal components of cybersecurity strategy, commencing with the pivotal function of encryption in safeguarding confidential information. An essential layer of security is provided by encryption methods and protocols, which prevent unauthorized users from reading data. Furthermore, the application of multi-factor authentication is examined as a crucial security precaution. The security of digital assets is greatly increased by MFA, which requires users to submit several kinds of authentication, such as passwords, biometric information, or security tokens. The study also explores the significance of intrusion prevention systems and intrusion detection systems in preventing unwanted access. IDS keeps an eye on networks for unusual behavior or security policy violations and instantly notifies administrators of any hazards. people can learn how to spot and avoid social engineering scams. Organizations may greatly lower the chance of successful cyber intrusions by arming users with knowledge. Finally This report concludes by emphasizing how urgent it is to address cybersecurity issues in the digital age. A proactive, multi-layered protection plan is essential due to the increasing sophistication and pervasiveness of cyber threats.

Dual physical stimuli fluorescence response of an α-cyanostilbene derivative for dynamic information storage

α-cyanostilbene derivatives, known as AIE molecules, have the potential to serve as optical recorders and vapor sensor by altering the molecular aggregation and / or stacking models. Despite the significant scientific and technological importance of the correlation between molecular stacking modes and modulated optical properties, relevant research remains scarce. Herein, two thermodynamically stable crystalline phase (BCr and GCr) were found for a biphenyl-based cyanostilbenic compound, DSB-SQ, condensed from its solution. Uniquely, the BCr demonstrated reversible mechanochromism and irreversible thermalchromism, whereas GCr remained unaffected by exposure to light, heat, grinding, etc. The discoloration process was correlated with the variation of local dipole coupling modes during the phase transition, leading to a significant change in π-π overlap and delocalization of excited states. Finally, the BCr was doped with polyvinyl alcohol (PVA) to prepare a composite membrane with dynamic information storage and encryption function.

Verifiable Encryption from MPC-in-the-Head

Verifiable encryption (VE) is a protocol where one can provide assurance that an encrypted plaintext satisfies certain properties, or relations. It is an important building block in cryptography with many useful applications, such as key escrow, group signatures, optimistic fair exchange, and others. However, the majority of previous VE schemes are restricted to instantiation with specific public-key encryption schemes or relations. In this work, we propose a novel framework that realizes VE protocols using zero-knowledge proof systems based on the MPC-in-the-head paradigm (Ishai et al. STOC 2007). Our generic compiler can turn a large class of zero-knowledge proofs into secure VE protocols for any secure public-key encryption scheme with the undeniability property, a notion that essentially guarantees binding of encryption when used as a commitment scheme. Our framework is versatile: because the circuit proven by the MPC-in-the-head prover is decoupled from a complex encryption function, the work of the prover is focused on proving the encrypted data satisfies the relation, not the proof of plaintext knowledge. Hence, our approach allows for instantiation with various combinations of properties about the encrypted data and encryption functions. We then consider concrete applications, to demonstrate the efficiency of our framework, by first giving a new approach and implementation to verifiably encrypt discrete logarithms in any prime order group more efficiently than was previously known. Then we give the first practical verifiable encryption scheme for AES keys with post-quantum security, along with an implementation and benchmarks.

Unbounded non-zero inner product encryption

In a non-zero inner product encryption (NIPE) scheme, ciphertexts and keys are associated with vectors from an inner-product space. Decryption of a ciphertext for x→ is allowed by a key for y→ if and only if the inner product 〈x→,y→〉≠0. Existing constructions of NIPE assume the length of the vectors are fixed apriori. We present the first constructions of unbounded non-zero inner product encryption (UNIPE) with constant sized keys. Unbounded here refers to the size of vectors not being pre-fixed during setup. Both constructions, based on bilinear maps, are proven selectively secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.Our constructions are obtained by transforming the unbounded inner product functional encryption (IPFE) schemes of Dufour-Sans and Pointcheval (ACNS 2019), one in the strict domain setting and the other in the permissive domain setting. Interestingly, in the latter case, we prove security from DBDH, a static assumption while the original IPFE scheme relied on an interactive parameterised assumption. In terms of efficiency, features of the IPFE constructions are retained after transformation to NIPE. Notably, the public key and decryption keys have constant size.

Differentially Private Functional Encryption

We address the question of realizing privacy preserving analysis of user data. The abstract scenario considered is that an analyst aims to evaluate a function f on some user data X. To achieve comprehensive privacy, it is necessary to protect the input X directly. However, it is known that f(X) may leak too much information about X as well. A common approach to mitigate such risks is to make the computation differential private. In practice, this is often accomplished by replacing f by a noisy variant f^*. We investigate the use of multi-input functional encryption (MIFE) for achieving input- and output-privacy in one cryptographic mechanism. In a MIFE scheme, a setup authority can generate restricted decryption keys which enable to learn specific functions of encrypted messages, without revealing any additional information. To achieve differential privacy in this process, we introduce as a new cryptographic primitive: noisy multi-input functional encryption (NMIFE). It extends the concept of MIFE such that the decryption key may also encode a noisy function where the noise value is secret. While the change from MIFE to NMIFE is rather straightforward, the challenge is to come up with precise and workable definitions of correctness and security definition that we propose and explain in this work. Here, the security definition is tailored to the use case of differential privacy. As it is a special case of the established notion of full-hiding security, we present a generic transformation that allows to turn any full-hiding MIFE scheme into a secure NMIFE scheme that has practically the same performance as the initial MIFE scheme. Moreover, we make use of the fact that the proposed security definition is less restrictive and present a new concrete NMIFE scheme for evaluating the inner product. It is dubbed DiffPIPE (short for DIFFerentially Private Inner Product Evaluation). DiffPIPE is not the result from the transformation and outperforms all from existing full-hiding MIFE schemes constructed NMIFE schemes. In experiments, we demonstrate its applicability for realizing privacy preserving counting queries on data sets.

Delegated Private Matching For Compute

Private matching for compute (PMC) establishes a match between two datasets owned by mutually distrusted parties (C and P) and allows the parties to input more data for the matched records for arbitrary downstream secure computation without rerunning the private matching component. The state-of-the-art PMC protocols only support two parties and assume that both parties can participate in computationally intensive secure computation. We observe that such operational overhead limits the adoption of these protocols to solely powerful entities as small data owners or devices with minimal computing power will not be able to participate. We introduce two protocols to delegate PMC from party P to untrusted cloud servers, called delegates, allowing multiple smaller P parties to provide inputs containing identifiers and associated values. Our Delegated Private Matching for Compute protocols, called DPMC and DsPMC, establish a join between the datasets of party C and multiple delegators P based on multiple identifiers and compute secret shares of associated values for the identifiers that the parties have in common. We introduce a rerandomizable encrypted oblivious pseudorandom function (OPRF) primitive, called EO, which allows two parties to encrypt, mask, and shuffle their data. Note that EO may be of independent interest. Our DsPMC protocol limits the leakages of DPMC by combining our EO scheme and secure three-party shuffling. Finally, our implementation demonstrates the efficiency of our constructions by outperforming related works by approximately 10x for the total protocol execution and by at least 20x for the computation on the delegators.

A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption

Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested.

Reinforcing privacy in cloud computing via adaptively secure non-zero inner product encryption and anonymous identity-based revocation in unbounded setting

Cloud computing serves as an advanced computing technology to support Internet services and has numerous applications, including medical fields, online data storage, social network, big data analysis, and e-learning platforms. Inner product encryption (IPE) is a promising cryptographic primitive that facilitates access control over outsourced encrypted data by restricting decryption capability via an inner product relation. Most of the existing IPEs are built assuming a priori bound on the length of attribute vectors associated with the plane data. A situation may occur when adding new attributes is essential due to upgradation. Thus it is desirable to construct IPE with variable length attribute vectors instead of fixing the length at the setup phase. Such IPEs are more flexible while encrypting data and consequently have more real-life applications. In the following, we make our contributions:–We design the first efficient adaptively payload-hiding secure unbounded non-zero IPE (UNIPE) scheme from the standard symmetric external Diffie-Hellman (SXDH) assumption and prove security in the standard model.–We propose a generic construction of an adaptively weak attribute-hidingUNIPE from an unbounded inner product functional encryption (UIPFE) scheme. We also instantiate UNIPE from the SXDH assumption and prove security in the standard model.–Finally, we present the first unbounded anonymous identity-based revocation (UAnon-IBRV) scheme using our generic UNIPE as the building block. The anonymity feature ensures that the ciphertext reveals no information about the revoking sets of the system.

Technology for using special encryption functions in PostgreSQL

Cryptographic encryption mechanisms in the open source PostgreSQL server DBMS are discussed. Special high-level and low-level functions of symmetric encryption of the Pgcrypto module for ensuring information security of data are analyzed. The analysis of special functions of the third-party GPG program is performed, which allow performing asymmetric encryption in the PostgreSQL database server. A combined data encryption technology in database tables is proposed, using a hierarchy of keys (asymmetric and symmetric encryption) with a passphrase, which is also encrypted to ensure better protection of encrypted data. This technology is being tested on the example of developing a function for encrypting data, which is triggered by a trigger when inserting rows into a database table, and a data decryption function when reading encrypted data from a table.

Blockchain based medical image encryption using Arnold’s cat map in a cloud environment

Improved software for processing medical images has inspired tremendous interest in modern medicine in recent years. Modern healthcare equipment generates huge amounts of data, such as scanned medical images and computerized patient information, which must be secured for future use. Diversity in the healthcare industry, namely in the form of medical data, is one of the largest challenges for researchers. Cloud environment and the Block chain technology have both demonstrated their own use. The purpose of this study is to combine both technologies for safe and secure transaction. Storing or sending medical data through public clouds exposes information into potential eavesdropping, data breaches and unauthorized access. Encrypting data before transmission is crucial to mitigate these security risks. As a result, a Blockchain based Chaotic Arnold’s cat map Encryption Scheme (BCAES) is proposed in this paper. The BCAES first encrypts the image using Arnold’s cat map encryption scheme and then sends the encrypted image into Cloud Server and stores the signed document of plain image into blockchain. As blockchain is often considered more secure due to its distributed nature and consensus mechanism, data receiver will ensure data integrity and authenticity of image after decryption using signed document stored into the blockchain. Various analysis techniques have been used to examine the proposed scheme. The results of analysis like key sensitivity analysis, key space analysis, Information Entropy, histogram correlation of adjacent pixels, Number of Pixel Change Rate, Peak Signal Noise Ratio, Unified Average Changing Intensity, and similarity analysis like Mean Square Error, and Structural Similarity Index Measure illustrated that our proposed scheme is an efficient encryption scheme as compared to some recent literature. Our current achievements surpass all previous endeavors, setting a new standard of excellence.