Privacy-preserving Communication Research Articles

Bilateral Proxy Federated Domain Generalization for Privacy-preserving Medical Image Diagnosis.

Contemporary domain generalization methods have demonstrated effectiveness in aiding the generalized diagnosis of medical images with multi-source data by joint optimization. However, the centralized training paradigm employed by these approaches becomes infeasible when data are non-shared across domains due to the high privacy of medical data. Despite attempts by existing federated domain generalization methods to address this issue, the simultaneous attainment of strict privacy protection and a satisfactory level of generalization ability on out-of-distribution data remains a persistent challenge. In this paper, to tackle this challenging problem, we propose a novel approach called the Bilateral Proxy Framework (BPF). The BPF leverages the client-side proxies to facilitate the strict privacy-preserving communications with the server and ensure smoother and more stable convergences of local models through mutual distillation. Meanwhile, the server-side proxy adopts a distance-based strategy and a parameter moving average scheme, which enhances the stability and robustness of the global model, particularly by averting abrupt parameter changes that could result in fluctuations or overfitting. Through these advancements, our framework strives to enhance the generalization capability of the global model, enabling more accurate and reliable medical image diagnosis in federated settings. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts on both simulated and real-world distribution medical image diagnosis tasks.

Survey of Recent Results in Privacy-Preserving Mechanisms for Multi-Agent Systems

Privacy-preserving communication in cooperative control is essential for effective operations of various systems where sensitive information needs to be protected. This includes systems such as smart grids, traffic management systems, autonomous vehicle networks, healthcare systems, financial networks, and social networks. Recent privacy-preserving cooperative control literature is categorized and discussed in this paper. Advantages and disadvantages of differential privacy and encryption-based privacy-preserving protocols are described. The objective of this work is to examine and analyze existing research and knowledge related to the preservation of privacy in the context of cooperative control. This paper aims to identify and present a range of approaches, techniques, and methodologies that have been proposed or employed to address privacy concerns in multi-agent systems. It seeks to explore the current challenges, limitations, and gaps in the existing literature. It also aims to consolidate the findings from various studies to provide an overview of privacy-preserving cooperative control in multi-agent systems. The goal is to assist in the development of novel privacy-preserving mechanisms for cooperative control.

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.

SL-PPCP: Secure and Low-Cost Privacy-Preserving Communication Protocol for Vehicular Ad Hoc Networks

SL-PPCP: Secure and Low-Cost Privacy-Preserving Communication Protocol for Vehicular Ad Hoc Networks

Recurrent neural networks-guided vector-valued synchronized key exchange for secure and privacy-preserving communication in Industrial Internet of Things

This paper presents an innovative approach to the problem of key exchange in the Industrial Internet of Things (IIoT) implementation. Recurrent Neural Networks (RNNs) and vector-valued neural synchronization are the key components of the proposed secure transmission method. Drive-response methods are integrated to improve speed in important applications, meeting the continuous need for effective cryptographic key exchange between IIoT devices. Conventional algorithms face drawn-out assessment procedures that jeopardize neural synchronization concealment. This work examines how random input vector generation and synchronization issues in RNNs with drive-response are affected by proportional and non-proportional delays. Furthermore, it delves into an unexplored domain, investigating the synchronization of response-based RNN systems without delays and drive-response-based RNN systems with various proportional delays. It also suggests a simplified analysis of Artificial Neural Networks (ANNs) synchronization, organizing ANNs for session key switch-over using an RNN system. To achieve polynomial and non-polynomial synchronization in the proposed driver response systems, this method offers several benefits: it introduces a polynomial synchronization theory for RNNs to generate synchronized input vectors for ANN synchronization; it uses inequality assessment techniques and Lyapunov formulas to derive relevant control inputs and time-dependent conditions; it establishes the relationship between polynomial and non-polynomial synchronization; and it provides numerical examples demonstrating its effectiveness. It also builds a neural network for generating session keys throughout the IIoT network by aligning vector-valued ANNs in a reciprocal manner. This strategy outperforms previous approaches in the literature, validated through simulations, balances resilience against attacks with minimal computational load, resulting in more effective and resilient industrial applications.

Lightweight and Secure Multi-Message Multi-Receiver Certificateless Signcryption Scheme for the Internet of Vehicles

The Internet of Vehicles (IoV) improves traffic efficiency and enhances driving safety through the real-time collection and analysis of traffic-related data. Numerous secure and privacy-preserving communication protocols have been proposed for the IoV. However, various security threats, privacy leakage, and inefficient communications remain unaddressed. Therefore, a lightweight and secure multi-message multi-receiver certificateless signcryption (LS-MRCLSC) scheme based on elliptic curve cryptography (ECC) is proposed. The proposed scheme guarantees secure communication and promotes messaging efficiency with multi-cast mode. Multiple key generation centers (KGCs) collaborate to generate and update the system master key (SMK) using Feldman’s verifiable secret-sharing (FVSS) algorithm, avoiding the single point of failure (SPoF) problem. Formal security proofs under the random oracle model (ROM) demonstrate that the proposed scheme meets requirements such as data confidentiality, message unforgeability, anonymity, and unlinkability. Performance evaluations confirm that the LS-MRCLSC scheme is better than similar schemes in terms of efficiency, feasibility, and scalability.

Secure and Lightweight Authentication Protocol for Privacy Preserving Communications in Smart City Applications

A smart city is a concept that leverages technology to improve the quality of life for citizens, enhance sustainability, and streamline urban services. The goal of a smart city is to use data and technology to manage resources and assets efficiently, make informed decisions, and create a more livable and thriving city for its residents. Smart cities rely on a range of technologies including the Internet of Things (IoT), Artificial Intelligence (AI), big data analytics, and cloud computing to gather, process, and analyze data from various sources. The aim is to create a city that is more connected, responsive, and sustainable, and that provides its residents with a better quality of life, opportunities, and services. A secure and efficient message communication protocol for sensitive information and real-time communication is critical for the functioning of a smart city environment. The main findings of this paper are to develop a new authentication protocol that meets the specific requirements and constraints of smart city applications. The message communication between smart cities is conducted with the help of a gateway. The challenge in constructing a working, viable infrastructure for a smart city is to provide secure authentication for message communication between the user and gateway node in one network, and the gateway node of one network to the gateway node of the other network. The objective for doing research to develop an authentication protocol that ensures the privacy and security of data transmitted in smart city applications while maintaining a lightweight and efficient design. This paper proposes a secure authentication protocol and key establishment scheme for access to the application in smart cities to make feasible access through the IoT environment. The proposed protocol ensures the mutual authentication between user and gateways, and the security analysis shows that the proposed protocol is effective against energy consumption and have less computational cost. The performance of the proposed method is analyzed and tested using BAN Logic and AVISPA security verification to confirm the authenticity of the security protocol. We do compare with past studies of which our proposed method outperformed.

Pairing-Free Certificateless Aggregate Signcryption Scheme for Vehicular Sensor Networks

Smart vehicle applications play an increasingly significant role in constructing intelligent transportation systems. Vehicles with sensors are able to form temporary vehicular sensor networks dynamically, in which a mass of data are collected, shared, and aggregated to the traffic administration for improving road safety and efficiency. In such networks, vehicles exchange urgent or sensitive information with others in open wireless channels. To ensure secure and privacy-preserving communications, we develop a new pairing-free certificateless aggregate signcryption scheme based on the elliptic curve cryptosystem. The certificateless setting prevents the proposed scheme from both certificate management and key escrow issues. Aggregate signcryption without pairings realizes the aggregation and simultaneous authentication of multisource data by efficient cryptographic computing. The proposed scheme is proved to achieve confidentiality and unforgeability with the random oracle model. The security analysis shows that the proposed scheme provides forward secrecy, immunity to various known attacks, and pseudonym-based conditional privacy for vehicles. The performance analysis demonstrates that it satisfies more security properties with higher computation efficiency and lower communication overhead than state-of-the-art schemes.

Privacy-Enhanced AKMA for Multi-Access Edge Computing Mobility

Multi-access edge computing (MEC) is an emerging technology of 5G that brings cloud computing benefits closer to the user. The current specifications of MEC describe the connectivity of mobile users and the MEC host, but they have issues with application-level security and privacy. We consider how to provide secure and privacy-preserving communication channels between a mobile user and a MEC application in the non-roaming case. It includes protocols for registration of the user to the main server of the MEC application, renewal of the shared key, and usage of the MEC application in the MEC host when the user is stationary or mobile. For these protocols, we designed a privacy-enhanced version of the 5G authentication and key management for applications (AKMA) service. We formally verified the current specification of AKMA using ProVerif and found a new spoofing attack as well as other security and privacy vulnerabilities. Then we propose a fix against the spoofing attack. The privacy-enhanced AKMA is designed considering these shortcomings. We formally verified the privacy-enhanced AKMA and adapted it to our solution.

Physical Layer Anonymous Precoding: The Path to Privacy-Preserving Communications

Next-generation systems aim to increase both the speed and responsiveness of wireless communications while supporting compelling applications such as edge-cloud computing, remote health, vehicle-to-infrastructure communications, etc. As these applications are expected to carry confidential personal data, ensuring user privacy becomes a critical issue. In contrast to traditional security and privacy designs that aim to prevent confidential information from being eavesdropped upon by adversaries, or learned by unauthorized parties, in this article we consider designs that mask the user's identity during communication, resulting in anonymous communications. In particular, we examine the recent interest in physical layer (PHY) anonymous solutions. This line of research departs from conventional higher layer anonymous authentication/encryption and routing protocols, and judiciously manipulates the signaling pattern of transmitted signals in order to mask the sender's PHY characteristics. We first discuss the concept of anonymity at the PHY, then illustrate a strategy that is able to unmask the sender's identity by analyzing his/her PHY information only – that is, signalling patterns and the inherent fading characteristics. Subsequently, we review the emerging area of anonymous precoding to preserve the sender's anonymity while ensuring high receiver-side signal-to-interference-plus-noise ratio for communication. This family of anonymous precoding designs represents a new approach to providing anonymity at the PHY, introducing a new dimension for privacy-preserving techniques.

A practical privacy-preserving communication scheme for CAMs in C-ITS

In a Collaborative Intelligent Transportation System (C-ITS), vehicles send collaborative awareness messages (CAMs) carrying information such as speed, position, and identity, to interact with other vehicles and to obtain transportation services. Traditionally, CAMs are broadcasted in plaintext over unsafe networks, which may leak sensitive information of the vehicle. On one hand, many existing works attempting to solve this problem focused on anonymous authentication, but ignored the confidentiality of CAMs. Conversely, some other attempts trying to keep the privacy of CAMs have backward security problems in key management or vehicle revocation. Therefore, in this paper, we present a practical privacy-preserving communication scheme for CAMs in C-ITS. To ensure the privacy of a vehicle's identity, our scheme mainly uses the re-randomizable PS signature method to realize anonymous authentication; and to meet backward security, we use the complete subtree method to realize both key management and vehicle revocation. In more details, our proposal fulfills more security and privacy requirements, such as correctness, anonymity, traceability, revocability, backward security and anti-illegal message dissemination, while also hiding the content of CAMs.

Fundamentals of Physical Layer Anonymous Communications: Sender Detection and Anonymous Precoding

In the era of big data, anonymity is recognized as an important attribute in privacy-preserving communications. The existing anonymous authentication and routing designs are applied at higher layers of networks, ignoring the fact that physical layer (PHY) also contains privacy-critical information. In this paper, we introduce the concept of PHY anonymity, and reveal that the receiver can unmask the sender’s identity by only analyzing the PHY information, i.e., the signaling patterns and the characteristics of the channel. We investigate two scenarios, where the receiver has more antennas than the sender in the strong receiver case, and vice versa in the strong sender case. For each scenario, we first investigate sender detection strategies at the receiver, and then we develop anonymous precoding to address anonymity while guaranteeing high signal-to-interference-plus-noise-ratio (SINR) for communications. In particular, an interference suppression anonymous precoder is first proposed, assisted by a dedicated transmitter-side phase equalizer for removing phase ambiguity. Afterwards, a constructive interference anonymous precoder is investigated to utilize inter-antenna interference as a beneficial element without loss of the sender’s anonymity. Simulations demonstrate that the anonymous precoders are able to preserve the sender’s anonymity and simultaneously guarantee high SINR, opening a new dimension on PHY anonymous designs.

On the Design of a Privacy-Preserving Communication Scheme for Cloud-Based Digital Twin Environments Using Blockchain

Digital twin technology is recently in the spotlight because of its potential applicability in business and industry. In digital twin environments, data generated from physical assets are transmitted to a remote server, which performs simulations through digital twins in a virtual space. Then, the simulation results can be shared with the data owner or other users. However, several challenges exist in the application of digital twin technology in the real world. One of the main challenges involves determining how to share real-time data for the simulation and how to share the simulation data securely. The data generated from physical assets may include sensitive information from data owners, and the leakage of data to an adversary can cause serious privacy problems. Moreover, the sharing of data with other data users should also be considered to maximize the availability of digital twin data. To resolve these issues, we propose a system model for the secure sharing of digital twin data. The proposed system model uses cloud computing for efficient data sharing and blockchain for data verifiability. We also propose communication schemes for the proposed model to guarantee privacy preservation and data security in wireless channels. We analyze the security of the proposed protocol using informal methods and formal methods such as BAN logic and the AVISPA simulation tool. Furthermore, we compare the proposed protocol with related protocols and demonstrate that the proposed scheme is applicable to digital twin environments.

COVID-19 and Your Smartphone: BLE-Based Smart Contact Tracing.

While contact tracing is of paramount importance in preventing the spreading of infectious diseases, manual contact tracing is inefficient and time consuming as those in close contact with infected individuals are informed hours, if not days, later. This article proposes a smart contact tracing (SCT) system utilizing the smartphone’s Bluetooth low energy signals and machine learning classifiers to automatically detect those possible contacts to infectious individuals. SCT’s contribution is two-fold: a) classification of the user’s contact as high/low-risk using precise proximity sensing, and b) user anonymity using a privacy-preserving communication protocol. To protect the user’s privacy, both broadcasted and observed signatures are stored in the user’s smartphone locally and only disseminate the stored signatures through a secure database when a user is confirmed by public health authorities to be infected. Using received signal strength each smartphone estimates its distance from other user’s phones and issues real-time alerts when social distancing rules are violated. Extensive experimentation utilizing real-life smartphone positions and a comparative evaluation of five machine learning classifiers indicate that a decision tree classifier outperforms other state-of-the-art classification methods with an accuracy of about 90% when two users carry their smartphone in a similar manner. Finally, to facilitate research in this area while contributing to the timely development, the dataset of six experiments with about 123 000 data points is made publicly available.

BPPCP: Block chain-based privacy-preserving communication protocol for VANETs

Vehicular Ad-hoc network (VANET) enhances traffic safety and efficiency by broadcasting traffic related messages wirelessly. Secure and safe communication among moving nodes of VANET, privacy, mutual authentication and non- repudiation are the most important security requirements. Therefore pre-serving these security goals, we develop an efficient andsecure communication system model for as a BPPCP Protocol for VANET. This system model work through cryptographic primitive scheme. In this system we provide security among moving nodes of VANET. This system uses an additional feature of Smart Content, which makes it unique from existing systems by providing some extra security features.

Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems.

Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup. It provides efficient training architecture, sufficient data, and privacy-preserving communication for boosting the performance and feasibility of ML algorithms. In this environment, the resultant global model produced by averaging various trained client models is vital. During each round of FL, model parameters are transferred from each client device to the server while the server waits for all models before it can average them. In a realistic scenario, waiting for all clients to communicate their model parameters, where client models are trained on low-power Internet of Things (IoT) devices, can result in a deadlock. In this paper, a novel temporal model averaging algorithm is proposed for asynchronous federated learning (AFL). Our approach uses a dynamic expectation function that computes the number of client models expected in each round and a weighted averaging algorithm for continuous modification of the global model. This ensures that the federated architecture is not stuck in a deadlock all the while increasing the throughput of the server and clients. To implicate the importance of asynchronicity in cybersecurity, the proposed algorithm is tested using NSL-KDD intrusion detection system datasets. The performance accuracy of the global model is about 99.5% on the dataset, outperforming traditional FL models in anomaly detection. In terms of asynchronicity, we get an increased throughput of almost 10.17% for every 30 timesteps.

A secure, efficient and verifiable multimedia data sharing scheme in fog networking system

As an emerging and efficient paradigm for multimedia systems, fog networking has attracted widespread attention over the last few years. However, an increasing number of attacks in the current virtualized environments underlines the importance of secure data sharing. Unfortunately, existing multimedia data sharing schemes are not suitable for the networking systems because of the heavy computational operations, latency-sensitive service, and resource-limited devices. Additionally, authenticated and secure communications are crucial issues related to privacy and trust. In this paper, we propose a secure and efficient data sharing scheme with the computation outsourcing capability in a fog networking system by employing ciphertext-policy attribute-based encryption. The scheme supports dynamic policy updating and delegates attribute revocation processes to the cloud and fog by proxy re-encryption. In particular, we build a secure communication protocol for the revocation parameter transmission. We adopt a chaotic map to generate a one-time key, by which the revocation parameters are encrypted. Then, to ensure legal user accessing to the system, we establish a privacy-preserving communication architecture between the user and cloud, which provides authentication. In addition, our scheme provides a verifiable auditing service for the decryption key and shared file, thus ensuring its correctness. Finally, we analyze the security of the scheme, evaluate its performance, and compare it with related works.

Enhanced Security Identity-Based Privacy-Preserving Authentication Scheme Supporting Revocation for VANETs

Existing conditional privacy-preserving communication protocols based on a tamper-proof device (TPD) in vehicular ad hoc networks (VANETs) face the challenges of master key compromise and vehicle revocation. To address these challenges, we propose a new identity-based anonymous authentication scheme. In this scheme, the master key of the system will not be directly installed in the TPD; hence, it is impossible for an adversary to obtain the system master key from the TPD. To generate the private key of the vehicle, an additional secret is required, and this secret is provided by the roadside unit (RSU). Therefore, revoking a malicious vehicle from the VANETs is efficient, since the RSU only needs to stop generating the up-to-date secret for the vehicle. In addition, the signature verification algorithm of the scheme involves no bilinear pairing operations, making the verification process more efficient. The security of the scheme is proved to be equivalent to the elliptic curve discrete logarithm assumption in the random oracle. The experimental results show that the authentication of the scheme is more efficient, and the communication cost has been diminished.

An Efficient and Privacy Protection Communication Scheme for Smart Grid

Smart grid is a modern electric power grid designed to improve efficiency and reliability of the production and distribution of electric power. In a smart grid, smart meters continuously generate electric power consumption data and send it to the server. These data have two important purposes: (1) The sum of the data generated by each meter in a certain period of time will be used for billing; (2) The sum of the data generated by all meters in a specific area at a given time will be used to predict the electric power required in that area for electric power distribution. These data are considered to be sensitive and should be properly protected. There have been many studies on the confidentiality and privacy protection of these data. Some schemes require trusted servers, some schemes require heavy computation, and some schemes need to send two sets of data, one for billing and the other for electric power distribution. In this article, we propose an efficient and privacy-preserving communication scheme for the transmission of meter data in a smart grid. No trusted authorities are required in the scheme. By sending only one set of data, the new communication scheme can ensure that both sums for billing and sums for electric power distribution can be computed accurately. The scheme uses only simple operations, such as addition and hashes. It is computationally lightweight and suitable for devices with limited computing resources.

VPPCS: VANET-Based Privacy-Preserving Communication Scheme

Over the past years, vehicular ad hoc networks (VANETs) have been commonly used in intelligent traffic systems. VANET's design encompasses critical features that include autonomy, distributed networking, and rapidly changing topology. The characteristics of VANET and its implementations for road safety have attracted considerable industry and academia interest, particularly in research involving transport systems enhancement that could potentially save lives. Message broadcasting in an open access system, such as VANET, is the main and utmost challenging problem with regard to security and privacy in VANETs. Various studies on VANET security and privacy have been proposed. Nevertheless, none has considered overall privacy requirements such as unobservability. In order to address these shortcomings, we propose a VANET based privacy-preserving communication scheme (VPPCS), which meets the requirements for content and contextual privacy. It leverages elliptic curve cryptography (ECC) and an identity-based encryption scheme. We have carried out a detailed security analysis (burrows-abadi-needham (BAN) logic, random oracle model, security of proof, and security attributes) to validate and verify the proposed scheme. The analysis has shown that our scheme is secure and also shown to be effective in a performance evaluation. The proposed scheme does not only meet the previously mentioned security and privacy requirements, but also impervious to various types of attacks such as replay, impersonation, modification, and man-in-the-middle attacks.