Functional Encryption Research Articles

SAMFL: Secure Aggregation Mechanism for Federated Learning with Byzantine-robustness by functional encryption

Federated learning (FL) enables collaborative model training without sharing private data, thereby potentially meeting the growing demand for data privacy protection. Despite its potentials, FL also poses challenges in achieving privacy-preservation and Byzantine-robustness when handling sensitive data. To address these challenges, we present a novel Secure Aggregation Mechanism for Federated Learning with Byzantine-Robustness by Functional Encryption (SAMFL). Our approach designs a novel dual-decryption multi-input functional encryption (DD-MIFE) scheme, which enables efficient computation of cosine similarities and aggregation of encrypted gradients through a single ciphertext. This innovative scheme allows for dual decryption, producing distinct results based on different keys, while maintaining high efficiency. We further propose TF-Init, integrating DD-MIFE with Truth Discovery (TD) to eliminate the reliance on a root dataset. Additionally, we devise a secure cosine similarity calculation aggregation protocol (SC2AP) using DD-MIFE, ensuring privacy-preserving and Byzantine-robust FL secure aggregation. To enhance FL efficiency, we employ single instruction multiple data (SIMD) to parallelize encryption and decryption processes. Concurrently, to preserve accuracy, we incorporate differential privacy (DP) with selective clipping of model layers within the FL framework. Finally, we integrate TF-Init, SC2AP, SIMD, and DP to construct SAMFL. Extensive experiments demonstrate that SAMFL successfully defends against both inference attacks and poisoning attacks, while improving efficiency and accuracy compared to existing methods. SAMFL provides a comprehensive integrated solution for FL with efficiency, accuracy, privacy-preservation, and robustness.

Decentralized Multi-Client Functional Encryption for Inner Product With Applications to Federated Learning

Decentralized Multi-Client Functional Encryption for Inner Product With Applications to Federated Learning

A quantum-safe authentication scheme for IoT devices using homomorphic encryption and weak physical unclonable functions with no helper data

Physical Unclonable Functions (PUFs) are widely used to authenticate electronic devices because they take advantage of random variations in the manufacturing process that are unique to each device and cannot be cloned. Therefore, each device can be uniquely identified and counterfeit devices can be detected. Weak PUFs, which support a relatively small number of challenge-response pairs (CRPs), are simple and easy to construct. Device authentication with weak PUFs typically uses helper data to obfuscate and recover a cryptographic key that is then required by a cryptographic authentication scheme. However, these schemes are vulnerable to helper-data attacks and many of them do not protect conveniently the PUF responses, which are sensitive data, as well as are not resistant to attacks performed by quantum computers. This paper proposes an authentication scheme that avoids the aforementioned weaknesses by not using helper data, protecting the PUF response with a quantum-safe homomorphic encryption, and by using a two-server setup. Specifically, the CRYSTALS-Kyber public key cryptographic algorithm is used for its quantum resistance and suitability for resource-constrained Internet-of-Things (IoT) devices. The practicality of the proposal was tested on an ESP32 microcontroller using its internal SRAM as a SRAM PUF. For PUF responses of 512 bits, the encryption execution time ranges from 16.41 ms to 41.08 ms, depending on the desired level of security. In terms of memory, the device only needs to store between 800 and 1,568 bytes. This makes the solution post-quantum secure, lightweight and affordable for IoT devices with limited computing, memory, and power resources.

Computational Differential Privacy for Encrypted Databases Supporting Linear Queries

Differential privacy is a fundamental concept for protecting individual privacy in databases while enabling data analysis. Conceptually, it is assumed that the adversary has no direct access to the database, and therefore, encryption is not necessary. However, with the emergence of cloud computing and the << on-cloud >> storage of vast databases potentially contributed by multiple parties, it is becoming increasingly necessary to consider the possibility of the adversary having (at least partial) access to sensitive databases. A consequence is that, to protect the on-line database, it is now necessary to employ encryption. At PoPETs'19, it was the first time that the notion of differential privacy was considered for encrypted databases, but only for a limited type of query, namely histograms. Subsequently, a new type of query, summation, was considered at CODASPY'22. These works achieve statistical differential privacy, by still assuming that the adversary has no access to the encrypted database. In this paper, we take an essential step further by assuming that the adversary can eventually access the encrypted data, making it impossible to achieve statistical differential privacy because the security of encryption (beyond the one-time pad) relies on computational assumptions. Therefore, the appropriate privacy notion for encrypted databases that we target is computational differential privacy, which was introduced by Beimel et al. at CRYPTO '08. In our work, we focus on the case of functional encryption, which is an extensively studied primitive permitting some authorized computation over encrypted data. Technically, we show that any randomized functional encryption scheme that satisfies simulation-based security and differential privacy of the output can achieve computational differential privacy for multiple queries to one database. Our work also extends the summation query to a much broader range of queries, specifically linear queries, by utilizing inner-product functional encryption. Hence, we provide an instantiation for inner-product functionalities by proving its simulation soundness and present a concrete randomized inner-product functional encryption with computational differential privacy against multiple queries. In terms of efficiency, our protocol is almost as practical as the underlying inner product functional encryption scheme. As evidence, we provide a full benchmark, based on our concrete implementation for databases with up to 1 000 000 entries. Our work can be considered as a step towards achieving privacy-preserving encrypted databases for a wide range of query types and considering the involvement of multiple database owners.

Confidential outsourced support vector machine learning based on well-separated structure

Support Vector Machine (SVM) has revolutionized various domains and achieved remarkable successes. This progress relies on subtle algorithms and more on large training samples. However, the massive data collection introduces security concerns. To facilitate secure integration of data efficiently for building an accurate SVM classifier, we present a non-interactive protocol for privacy-preserving SVM, named NPSVMT. Specifically, we define a new well-separated structure for computing gradients that can decouple the fusion matter between user data and model parameters, allowing data providers to outsource the collaborative learning task to the cloud. As a result, NPSVMT is capable of removing the multiple communications and eliminating the straggler’s effect (waiting for the last), thereby going beyond those developed with interactive methods, e.g., federated learning. To further decrease the data traffic, we introduce a high-efficient coding method to compress and parse training data. In addition, unlike outsourced schemes based on homomorphic encryption or secret sharing, NPSVMT exploits functional encryption to maintain the data confidentiality, achieving dropout-tolerant secure aggregation. The implementations verify that NPSVMT is faster by orders of magnitude than the existing privacy-preserving SVM schemes on benchmark datasets.

Stegocrypt: A robust tri‐stage spatial steganography algorithm using TLM encryption and DNA coding for securing digital images

AbstractThis research work presents a novel secured spatial steganography algorithm consisting of three stages. In the first stage, a secret message is divided into three parts, each is encrypted using a tan logistic map encryption key with a unique seed value. In the second stage, the encrypted parts are transformed into quick response codes, serving as a layer of channel coding. Subsequently, the quick response codes are decoded back into bit‐streams. To enhance security, a uniquely‐seeded Mersenne Twister key is generated and employed to apply DNA coding onto each bit‐stream. The resulting bit‐streams are then embedded in the least significant bits of the RGB channels of a cover image. Finally, the RGB channels are merged to form a single stego image. A comprehensive set of experimental analyses is conducted to evaluate the performance of the proposed secure steganography algorithm. The experimental results demonstrate the algorithm's robustness against various attacks and its ability to achieve high embedding capacity while maintaining imperceptibility. The proposed algorithm offers a promising solution for secure information hiding in the spatial domain, with potential applications in areas such as data transmission, digital forensics, and covert communication.

Janus Swarm Metamaterials for Information Display, Memory, and Encryption.

Metamaterials are emerging as an unconventional platform to perform computing abstractions in physical systems by processing environmental stimuli into information. While computation functions have been demonstrated in mechanical systems, they rely on compliant mechanisms to achieve predefined states, which impose inherent design restrictions that limit their miniaturization, deployment, reconfigurability, and functionality. Here, a metamaterial system is described based on responsive magnetoactive Janus particle (MAJP) swarms with multiple programmable functions. MAJPs are designed with tunable structure and properties in mind, that is, encoded swarming behavior and fully reversible switching mechanisms, to enable programmable dynamic display, non-volatile and semi-volatile memory, Boolean logic, and information encryption functions in soft, wearable devices. MAJPs and their unique swarming behavior open new functions for the design of multifunctional and reconfigurable display devices, and constitute a promising building block to develop the next generation of soft physical computing devices, with growing applications in security, defense, anti-counterfeiting, camouflage, soft robotics, and human-robot interaction.

Enhancing Privacy on Online Social Networs: A comparative study of encryption techniques

The ability to connect with people all over the world and share private information has made online social networks (OSNs) indispensable to contemporary communication. Because of the serious privacy and security concerns that come with their fast growth, protecting user data is of the utmost importance. In order to improve OSN privacy, this study examines several encryption techniques. It discusses symmetric encryption (ECC and RSA) and synthetic encryption (AES) as well as hybrid methods that combine the two types of encryption. Using examples from well-known sites such as LinkedIn, Facebook, and Twitter, the research assesses the efficacy and practicality of various strategies. Key management, performance implications, and user experience maintenance are some of the challenges that are addressed while adopting comprehensive encryption. To further improve privacy protection in OSNs, the paper delves into upcoming developments in encryption technology, such as decentralized encryption approaches and algorithms that are resistant to quantum computing. In order to better understand how to improve OSN security, this article will provide a thorough examination of encryption's function in protecting user privacy.

Biometric holographic encryption and authentication with multiple optics-biology keys based on inhomogeneous media optics

Biometric authentication has been widely used because of its uniqueness, accuracy, and versatility, but existing methods still have some shortcomings. Because biometric data is stored in the database, once the database is invaded, it will cause a large number of biometric data leakage and illegal use of huge risks; The cost of traditional feature extraction and matching strategy is too high; Biometric is only used for authentication, and its single function cannot meet the increasing security requirements; Methods that rely solely on biometric keys are not secure enough. To overcome these shortcomings, we propose the biometric holographic encryption and authentication with multiple optics-biology keys based on inhomogeneous media optics. The proposed method innovatively combines biometrics and inhomogeneous media optics to achieve dual functions of encryption and authentication. Instead of storing biometric data directly, the encryption and authentication information is encrypted as computer generated holograms in this method and stored in the database to reduce the risk of biometric data leakage. This method has low cost without complicated feature extraction and comparison authentication process. The dual functions of authentication and encryption is more practical. Multiple optics-biology keys improve the security. The security and robustness are analyzed. A graphical user interface (GUI) is designed for the method to promote its application. This work provides new methods and new ideas for the field of optics encryption and authentication, and will certainly have a broad application prospect.

Polymer-Based Room-Temperature Phosphorescence Materials Exhibiting Emission Lifetimes up to 4.6 s under Ambient Conditions.

The long-emission-lifetime nature of room-temperature phosphorescence (RTP) materials lays the foundation of their applications in diverse areas. Despite the advantage of mechanical property, processability and solvent dispersity, the emission lifetimes of polymer-based room-temperature phosphorescence materials remain not particularly long because of the labile nature of organic triplet excited states under ambient conditions. Specifically, ambient phosphorescence lifetime (τP) longer than 2 s and even 4 s have rarely been reported in polymer systems. Here, luminescent compounds with small phosphorescence rate on the order of approximately 10-1 s-1 are designed, ethylene-vinyl alcohol copolymer (EVOH) as polymer matrix and antioxidant 1010 to protect organic triplets are employed, and ultralong phosphorescence lifetime up to 4.6 s under ambient conditions by short-term and low-power excitation are achieved. The resultant materials exhibit high afterglow brightness, long afterglow duration, excellent processability into large area thin films, high transparency and thermal stability, which display promising anticounterfeiting and data encryption functions.

Achieving Image Encryption Quantum Dot-Functionalized Encryption Camera with Designed Films.

The risk of information leaks increases as images become a crucial medium for information sharing. There is a great need to further develop the versatility of image encryption technology to protect confidential and sensitive information. Herein, using high spatial redundancy (strong correlation of neighboring pixels) of the image and the in situ encryption function of a quantum dot functionalized encryption camera, in situ image encryption is achieved by designing quantum dot films (size, color, and full width at half maximum) to modify the correlation and reduce spatial redundancy of the captured image during encryption processing. The correlation coefficients of simulated encrypted image closely apporach to 0. High-quality decrypted images are achieved with a PSNR of more than 35dB by a convolutional neural network-based algorithm that meets the resolution requirements of human visual perception. Compared with the traditional image encryption algorithms, chaotic image encryption algorithms and neural network-based encryption algorithms described previously, it provides a universal, efficient and effective in situ image encryption method.

Improved unbounded inner-product functional encryption

In this paper, we propose an unbounded inner-product functional encryption (unbounded IPFE) scheme with semi-adaptive simulation-based security. Compared with the previous semi-adaptive secure scheme proposed by Tomida and Takashima [Asiacrypt18], our scheme enjoys about 28% shorter ciphertext and about 43% shorter secret key.Technically, we start with a bounded separable one-key IPFE scheme. In the separable one-key IPFE scheme, the public key and ciphertext can be divided into some vectors. At the same time, we develop a new transformation from a bounded separable one-key IPFE scheme towards an unbounded IPFE scheme. Finally, we give a concrete instantiation with the bounded separable one-key IPFE scheme and the transformation.

Compact FE for unbounded attribute-weighted sums for logspace from SXDH

This paper presents the first functional encryption (FE)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\ extsf{FE})$$\\end{document} scheme for the attribute-weighted sum functionality that supports the uniform model of computation. In such an FE scheme, encryption takes as input a pair of attributes (x, z) where x is public and z is private. A secret key corresponds to some weight function f, and decryption recovers the weighted sum f(x)z. In our scheme, both the public and private attributes can be of arbitrary polynomial lengths that are not fixed at system setup. The weight functions are modelled as Logspace Turing machines\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {Logspace Turing machines}$$\\end{document}. Prior schemes could only support non-uniform Logspace. The proposed scheme is proven adaptively simulation secure under the well-studied symmetric external Diffie–Hellman assumption against an arbitrary polynomial number of secret key queries both before and after the challenge ciphertext. This is the best possible security notion that could be achieved for FE. On the technical side, our contributions lie in extending the techniques of Lin and Luo [EUROCRYPT 2020] devised for indistinguishability-based payload hiding attribute-based encryption for uniform Logspace access policies and the “three-slot reduction” technique for simulation-secure attribute-hiding FE for non-uniform Logspace devised by Datta and Pal [ASIACRYPT 2021] to the context of simulation-secure attribute-hiding FE for uniform Logspace.

Ultraviolet encryption information storage using hydrophobic poly(ε-caprolactone)/carbon quantum dot composite film

Luminescent poly(ε-caprolactone)/nitrogen-doped carbon quantum dots (PCL/N-CQDs) film with the function of information protection and encryption was realized based on the fluorescent N-CQDs. The N-CQDs were synthesized from citric acid with urea as a dopant for the enhancement of fluorescent properties. The facilely prepared composite film retained the hydrophobic nature of PCL and achieved the enhanced mechanical property. The appropriate crystallization temperature and degree of crystallinity of PCL provide the film with opaque characteristics at room temperature, rendering it an ideal carrier for carrying information. The information encryption and decryption are achieved through ionoprinting technique, exploiting the ability of Fe3+ ions to quench the fluorescence of N-CQDs, allowing for the encryption of designed information on the film’s surface. With its opaque, hydrophobic, and photoluminescent properties, the PCL/N-CQDs composite film holds significant promise for applications in information encryption.

Fine-grained polynomial functional encryption

In this work, we present fine-grained secure polynomial functional encryption (PFE) for degree d≥1 over a field F: a ciphertext encrypts x∈Fn, a key is associated with a degree-d polynomial P and decryption recovers P(x)∈F. Fine-grained cryptographic primitives are secure against a resources bounded class of adversaries and computed by honest users with less resources than adversaries. In this paper, we construct the fine-grained PFE in these two fine-grained settings:(1) NC1 PFE: Based on the worst-case assumption NC1⊊⊕L/poly, we construct public-key polynomial functional encryption and achieve (i) selective simulation-based security and (ii) static function-hiding against adversary in NC1 where all honest algorithms are computable in AC0[2] and ciphertext sizes are O(n).(2) AC0 PFE: We construct a private-key polynomial functional encryption achieve unconditionally selective simulation-based security against adversary in AC0 where all honest algorithms are computable in AC0 and ciphertext sizes are O(n).

Decentralized Multi-Client Functional Encryption with Strong Security

Decentralized Multi-Client Functional Encryption (DMCFE) extends the basic functional encryption to multiple clients that do not trust each other. They can independently encrypt the multiple plaintext-inputs to be given for evaluation to the function embedded in the functional decryption key, defined by multiple parameter-inputs. And they keep control on these functions as they all have to contribute to the generation of the functional decryption keys. Tags can be used in the ciphertexts and the keys to specify which inputs can be combined together. As any encryption scheme, DMCFE provides privacy of the plaintexts. But the functions associated to the functional decryption keys might be sensitive too (e.g. a model in machine learning). The function-hiding property has thus been introduced to additionally protect the function evaluated during the decryption process. In this paper, we provide new proof techniques to analyze a new concrete construction of function-hiding DMCFE for inner products, with strong security guarantees: the adversary can adaptively query multiple challenge ciphertexts and multiple challenge keys, with unbounded repetitions of the same tags in the ciphertext-queries and a fixed polynomially-large number of repetitions of the same tags in the key-queries. Previous constructions were proven secure in the selective setting only.

Ad Hoc Broadcast, Trace, and Revoke

Traitor tracing schemes [Chor–Fiat–Naor, Crypto ’94] help content distributors fight against piracy and are defined with the content distributor as a trusted authority having access to the secret keys of all users. While the traditional model caters well to its original motivation, its centralized nature makes it unsuitable for many scenarios. For usage among mutually untrusted parties, a notion of *ad hoc* traitor tracing (naturally with the capability of broadcast and revocation) is proposed and studied in this work. Such a scheme allows users in the system to generate their own public/secret key pairs, without trusting any other entity. To encrypt, a list of public keys is used to identify the set of recipients, and decryption is possible with a secret key for any of the public keys in the list. In addition, there is a tracing algorithm that given a list of recipients’ public keys and a pirate decoder capable of decrypting ciphertexts encrypted to them, identifies at least one recipient whose secret key must have been used to construct the said decoder. Two constructions are presented. The first is based on functional encryption for circuits (conceptually, obfuscation) and has constant-size ciphertext, yet its decryption time is linear in the number of recipients. The second is a generic transformation that reduces decryption time at the cost of increased ciphertext size. A matching lower bound on the trade-off between ciphertext size and decryption time is shown, indicating that the two constructions achieve all possible optimal trade-offs, i.e., they fully demonstrate the Pareto front of efficiency. The lower bound also applies to broadcast encryption (hence all mildly expressive attribute-based encryption schemes) and is of independent interest.

Multidimensional Encryption by Chip-Integrated Metasurfaces.

Facing the challenge of information security in the current era of information technology, optical encryption based on metasurfaces presents a promising solution to this issue. However, most metasurface-based encryption techniques rely on limited decoding keys and struggle to achieve multidimensional complex encryption. It hinders the progress of optical storage capacity and puts encryption security at a disclosing risk. Here, we propose and experimentally demonstrate a multidimensional encryption system based on chip-integrated metasurfaces that successfully incorporates the simultaneous manipulation of three-dimensional optical parameters, including wavelength, direction, and polarization. Hence, up to eight-channel augmented reality (AR) holograms are concealed by near- and far-field fused encryption, which can only be extracted by correctly providing the three-dimensional decoding keys and then vividly exhibit to the authorizer with low crosstalk, high definition, and no zero-order speckle noise. We envision that the miniature chip-integrated metasurface strategy for multidimensional encryption functionalities promises a feasible route toward the encryption capacity and information security enhancement of the anticounterfeiting performance and optically cryptographic storage.

Privacy-Preserving Decentralized Functional Encryption for Inner Product

To support secure data mining and privacy-preserving computation, partial access and selective computation on encrypted data are desirable. Functional encryption (FE) is a new paradigm of public-key encryption and allows authorized users to compute specific functions on encrypted data without knowing the data. However, in some FE schemes, a trusted central authority (CA) is required to generate secret keys for users according to the description of functions. In this paper, to reduce trust on the CA and protect users' privacy, a privacy-preserving decentralised FE for inner product (PPDFEIP) scheme is proposed where multiple authorities co-exist and work independently without any interaction. Especially, to resist collusion attacks, all secret keys of the same user are tied to his/her global identifier (GID), but authorities cannot know any information of the GID even if they collaborate. We formalize the definition and security model of our PPFEIP scheme, and propose a concrete construction. Furthermore, the proposed scheme is implemented and evaluated. Finally, the security of our PPDFEIP scheme is reduced to well-known complexity assumptions. The novelty is to reduce trust on the CA, protect users' privacy and enable authorized users to compute inner product on encrypted data without compromising confidentiality.

Three Rounds Cryptography to Protect Secret Message

A simple three rounds method of message cryptography will be introduced, the message will be encrypted-decrypted by apply XORing operation of the message with the secret KEY1 in the first round, in the second round the message will be shuffled using the secret key KEY2, while in the third round the message binary matrix will be rotated left to a number of selected digits. The introduced method will be very secure it will use a secret kept digital color image as an image_key, this image will be used to generate KEY1 and KEY2. KEY1 will be extracted from the image from a selected position, while KEY2 will be an indices key obtained by sorting a number of bytes extracted from the image from another position determined by the user. In addition to the image_key the method will use the values of POS1, POS2, and NORLD to determine the starting position of key1, the starting position of key2 and the number of rotate left digits. The produced decrypted message will be very sensitive to the selected image_key, POS1, POS2 and NORTD. The encryption function will be simplified and it will be implemented using three simple tasks: XORing, shuffling and rotating left, while the decryption function will be implemented by applying rotate left, shuffling back and XORing operations. The proposed method will be implemented and tested using various messages, the results will be studied and analyzed to prove the achievements provided by the method in: quality, speed, security and sensitivity.