Leakage Of Private Information Research Articles

VCFL: A verifiable and collusion attack resistant privacy preserving framework for cross-silo federated learning

To prevent privacy information leakage through model parameters in federated learning, many works use homomorphic encryption to protect clients’ updates. However, most of them result in significant computation and communication overhead. Even worse, few of them have considered the correctness of the aggregated results and collusion attack between internal curious clients and the server. In this paper, we propose VCFL, an efficient verifiable and collusion attack resistant privacy preserving framework for cross-silo federated learning. Firstly, we design a homomorphic signcryption mechanism to sign and encrypt model parameters in one go. Secondly, we employ the blinding technique to resist collusion attack between clients and the server. Moreover, we leverage the batching approach to further reduce its computation and communication overhead. Finally, we simulate VCFL in FedML on real world datasets and models. Extensive experimental results show that VCFL can guarantee model performance while protecting privacy, and it is more efficient in both computation and communication than similar frameworks.

Naive Bayes Algorithm Mining Mobile Phone Trojan Crime Clues

After the mobile phone virus infects the mobile phone, it can transmit the real-time information of the user to the designated place set by the virus through the built-in recorder and camera on the mobile phone, thereby causing information leakage. With the rapid development of the Internet, the penetration rate of mobile terminals is also increasing day by day. As an emerging mobile terminal, smart phones have now fully occupied the market. With this trend, the importance of mobile phone information security is also increasing day by day. How to prevent mobile phone virus has gradually become an important issue. Trojan horse crime cases have different manifestations and behavioral characteristics from traditional cases. They have the characteristics of low crime cost, high income, high concealment, novel criminal methods, and great difficulty in detection, which brings greater difficulties to the public security organs in their investigation and detection. And the current research on mobile phone virus behavior is still in the preliminary stage, and some existing detection models can only target random networks. Trojan horses, viruses, and malicious software for smartphones have sprung up like mushrooms after rain, seriously infringing on the data security of mobile communication terminals, such as mobile phones and causing incalculable losses to users. This paper proposes a naive Bayesian algorithm to mine the clues of the criminal cases of mobile phone Trojans. It helps detect and discover new viruses at the beginning of an attack, allowing them to be more effectively defended and contained. And based on the feature set data extracted from the network data packets, it conducts an in-depth analysis of the current business behaviors of mobile phone Trojans, such as propagation and implantation, remote control, leakage of user privacy information, and malicious ordering, and extracts its behavior characteristics. Thus, unknown mobile Trojan horses that are taking place can be detected. The experimental results of the naive Bayesian classification algorithm proposed in this paper show that the algorithm improves the accuracy of mobile phone Trojan virus mining by 28%, which plays a significant role.

Guest Editors’ Introduction: Special Issue on 2021 Top Picks in Hardware and Embedded Security

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hardware is the</b> foundation of many systems ranging from embedded systems, and Internet of Things devices, to cyber–physical systems. The increasing design complexity of hardware continues to challenge our ability to provide robust security guarantees, thereby undermining the security of systems and resulting in security breaches and leakage of private information. The direct and indirect costs of addressing security vulnerabilities (e.g., root cause analysis, deploying fixes and mitigations, and risk of product recalls) not only damage the reputation of a company, but also prolong time-to-market deadlines, thereby squeezing the supply chain. To this end, researchers from academia and industry have been developing tools and techniques that can help identify and mitigate security issues in hardware, thereby building a bedrock for system security. One important task toward this ambitious goal is to identify the best set of attack and defense tools and techniques in hardware and embedded security, which typically spans many communities ranging from devices to circuits to architecture to CAD to cryptography. This special issue presents the articles selected during the third edition of the workshop “Top Picks in Hardware and Embedded Security” (shortly, Top Picks) held virtually (due to COVID-19) on November 5, 2021, “co-located” with the IEEE/ACM International Conference on Computer- Aided Design.

SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer

The user equipment and directory server stay in the state of asymmetric information in anonymous networks, so that it is hard to coordinate information leakage prevention and information sharing when we explore the routing addressing technology. The severe security risk faced by existing anonymous networks, such as Tor and I2P, is the leakage of privacy information of routing nodes. This paper tries to resolve this problem and proposes a novel secure querying scheme of the routing table (SQRT) based on oblivious transfer, which can effectively ensure that both user equipment and directory server faithfully follow the routing querying protocol and protect the privacy information of both parties to the greatest extent. The SQRT scheme can realize that the directory server cannot only send the required routing nodes to the user equipment, but can also ensure that the directory server cannot know the exact routing nodes received by the user equipment and protect the information of other routing nodes in the directory server from disclosure. Security analysis shows that in the scenario where the directory server and user equipment are semi-honest, respectively, the SQRT scheme can ensure the privacy of both directory server and user equipment. The experimental simulation results show that compared with existing schemes, the SQRT scheme has obvious performance advantages in terms of the degree of anonymity, protocol running time, and communication traffic.

An Efficient Multi-dimensional Data Aggregation Scheme for Fog-Assisted Wireless Body Area Network

The rapid development of wireless body area network and monitoring technology has brought new opportunities for the later rehabilitation of patients. Due to the limited computing power and power of wireless body area network (WBAN) sensor nodes, combining WBAN with fog computing is one of the feasible solutions for remote monitoring. Wireless body area network can exchange medical information in real time, which is an effective way to improve medical services. The WBAN is inefficient and vulnerable to attacks, leading to leakage of user privacy information. This paper proposes an efficient data aggregation scheme for fog-assisted WBAN. The analysis shows that the scheme can resist the existing attack. The security analysis shows that the scheme meets the privacy and security requirements of data aggregation in WBAN. Compared with existing schemes, this scheme has an efficiency advantage.

Differentially private containment control for multi-agent systems

This paper is concerned with the differential privacy-based containment control problem for a class of discrete-time multi-agent systems (MASs) with time-varying topology. The classical containment control implementation relies on the explicit information exchange between agents, which will lead to the possible leakage of agents' private information, especially the initial states. In this paper, to prevent such data disclosure to neighbours inside the MASs and the outside eavesdroppers, uncorrelated noise is injected into the data during information transmission, which will bring about a more complicated scenario to the containment control. Under a milder assumption on the connectivity of the MAS – ‘average graph topology’, by resorting to the Lyapunov stability theory and algebraic graph theory, and utilising stochastic analysis techniques, sufficient conditions are derived to ensure that the discrete-time MAS subjecting to the privacy protection mechanism can achieve bounded containment control. Then, convergence accuracy is studied from the viewpoint of probability. Specially, Markov inequality is first utilised to estimate the lower bound of the probability that followers' states converge to the neighbourhood of the convex hull formed by leaders' states. Besides, differential privacy analysis is carried out to verify the validity of the proposed privacy protection mechanism in protecting the followers' initial states. Finally, two numerical examples are simulated to verify the theoretical results.

Joint Protection of Energy Security and Information Privacy for Energy Harvesting: An Incentive Federated Learning Approach

Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energy-information cross threats, facing the overlapping vulnerability of energy deprivation and private information leakage. Although some existing works touch on the security of energy and information in EH, they treat these two issues independently, without collaborative and intelligent protection cross the energy side and information side. To address the aforementioned challenge, this article proposes a joint protection framework of energy security and information privacy for EH with an incentive federated learning approach. First, we design a federated-learning-based malicious energy user detection method according to energy status and behaviors to provide energy security protection. Second, a differential-privacy-empowered information preservation scheme is devised, where sensitive information is perturbed and protected by the customized demand-based noise. Third, a noncooperative-game-enabled incentive mechanism is established to encourage EH nodes to participate in the joint energy-information protection system. The proposed incentive mechanism derives the optimal energy-information security strategy for EH nodes and achieve a tradeoff between the protection of energy security and information privacy. Evaluation results have verified the effectiveness of our proposed joint protection mechanism.

Source node detection in social networks based on Trust–GMLA algorithm

In order to solve the problem of source node detection when privacy information leakage occurs in social networks, especially in the case of asynchronous propagation and low forwarding rate, this paper proposes a single source node detection algorithm named Trust–GMLA(Trust-Gradient Maximum Likelihood Algorithm) based on trust network and GMLA(gradient maximum likelihood algorithm). According to the randomness of asynchronous propagation, the hysteresis matrix of GMLA algorithm is reconstructed, and the matrix parameters are automatically adjusted according to the forwarding rate, which improves the success rate of asynchronous diffusion source node detection. Also, aiming at the problem that a large number of repeated paths need to be calculated in GMLA, a trust inference matrix is proposed to infer the trust value between nodes, determine the trust propagation path, reduce unnecessary path calculation, and improve the speed of the algorithm. Experiments on several social networks datasets show that the proposed Trust–GMLA algorithm can effectively improve the source node detection accuracy and speed.

User power allocation and relay beamforming design for secrecy sum rate maximization in two-way relay networks

In this paper, we investigate the physical-layer security (PLS) for multiple peer-to-peer communications in a two-way relaying networks involving 2 L single-antenna users, an N R -antenna relay, and an N E -antenna eavesdropper. Only a pair of peer-to-peer users demand security to exchange messages because the eavesdropper intends to wiretap them in both transmission phases. Our goal is to maximize the achievable secrecy sum rate (SSR) of specific peer-to-peer users, provided that restrictive constraints are satisfied. We endorse a null space beamforming technique at the relay to simplify the optimization problem and exclude private information leakage. The main problem is still very complicated due to coupled variables of the user transmit power with the relay beamforming matrix. Therefore, we convert the main problem into two subproblems, one in terms of the relay beamforming matrix and the other in terms of the user transmit power. To find a locally optimal solution for the relay beamforming, we turn the main problem into a difference of convex (DC) programming problem and for user power allocation, we drive the main problem into a constrained nonlinear optimization problem. Finally, simulations are performed to evaluate the performance benefits of the proposed method.

Real‐time facial expression recognition based on iterative transfer learning and efficient attention network

Real-time facial expression recognition is the basis for computers to understand human emotions and detect abnormalities in time. To effectively solve the problems of server overload and privacy information leakage, a real-time facial expression recognition method based on iterative transfer learning and efficient attention network (EAN) for edge resource-constrained scenes is proposed in this paper. Firstly, an EAN is designed with its parameter number and computation amount strictly limited by depth separable convolution and local channel attention mechanism. Then, the soft labels of facial expression data were obtained by EAN based on the idea of knowledge distillation, so as to provide more supervision information for the training process. Finally, an iterative transfer learning method of teacher-student (T-S) network was proposed; it refines the soft labels of the teacher network and further improves the recognition accuracy of the student network. The tests on the public datasets, FER2013 and RAF-DB, show that this method can significantly reduce the model complexity and achieve high recognition accuracy. Compared with other advanced methods, the proposed method strikes a good balance between complexity and accuracy, and well meets the real-time deployment requirements of facial expression recognition technology for edge resource-constrained scenes.

Differentially private self-normalizing neural networks for adversarial robustness in federated learning

The need for robust, secure and private machine learning is an important goal for realizing the full potential of the Internet of Things (IoT). Federated Learning has proven to help protect against privacy violations and information leakage. However, it introduces new risk vectors which make Machine Learning models more difficult to defend against adversarial samples. We consider the problem of improving the resilience of Federated Learning to adversarial samples without compromising the privacy preserving features which Federated Learning was primarily designed to provide.Common techniques such as adversarial training, in their basic form are not well suited for a Federated Learning environment. Our study shows that adversarial training, while improving adversarial robustness, comes at a cost of reducing the privacy guarantee of Federated Learning.We introduce DiPSeN, a Differentially Private Self-normalizing Neural Network which combines elements of differential privacy, self-normalization, and a novel optimization algorithm for adversarial client selection. Our empirical results on publicly available datasets for intrusion detection and image classification show that DiPSeN successfully improves both adversarial robustness in Federated Learning, while maintaining the privacy preserving characteristics of Federated Learning.

The Application of Communication Privacy Management Theory to Health Information on COVID-19 Through Social Media among Young Women

Background: Social media is open to inappropriate access, misuse, and disclosure of health data. The increasing public anxiety about the COVID-19 outbreak was triggered by the spread of hoax news. This research applies the framework of Communication Privacy Management Theory (CPM) for analyzing the disclosure of health information about COVID-19 through social media. This study aimed to explore health information on COVID-19 through social media in young women. Subjects and Method: This was a qualitative study with a phenomenological approach. The study was conducted in Surakarta, Central Java, from March - August 2021. Study participantss involving 10 women aged 23-24 year as participants. The study dimen­sions consist of privacy ownership within the boundaries of privacy, privacy control through privacy rules, and turbulence of health informa­tion privacy regarding COVID-19. The data were collected by literature review and in-depth interview. Results: Privacy ownership is privacy right and can be disclosed by the authorized only. COVID-19 patients’ status should be disclosed for tracing and finding convalescence plasma donor. Disclosure is useful to increase knowledge, correct information, influence people, and appeal preventive and curative attempts. Disclosure is useful to increase knowledge, correct information, influence people, and appeal preventive and curative attempts. Disclosure should consider different genders, situations, expectations, cultures, situational and condition demand, and ethics. Criteria of privacy border relate to urgency and need. Permeability always changes, and situation can lead to private border removal. Posting privacy information needs to crosscheck truth, consider privacy right, and privacy setting. Privacy control is required to avoid abuse. Privacy turbulence results from private rule infringement, private and public information bias, hoax, private information leakage, gossip, etc. Socialization, system improvement and supervision, and sanction imposition are needed. Conclusion: CPM theory can be used to explore private information on COVID-19. Keywords: communication privacy management, disclosure, social media, phenomenology Correspondence: Muhammad Agung Diponegoro. Masters Program in Communication Sciences, Faculty of Social and Political Sciences, Universitas Sebelas Maret Email: abdiaghoenk@gmail.com. Mobile: +6221­4281288. Journal of Health Policy and Management (2022), 07(01): 14-23 https://doi.org/10.26911/thejhpm.2022.07.01.02

Cost and Efficiency Analysis of Steganography in the IEEE 802.11ah IoT Protocol

The widespread use of the Internet of Things (IoT) applications has enormously increased the danger level of data leakage and theft in IoT as data transmission occurs through a public channel. As a result, the security of the IoT has become a serious challenge in the field of information security. Steganography on the network is a critical tool for preventing the leakage of private information and enabling secure and encrypted communication. The primary purpose of steganography is to conceal sensitive information in any form of media such as audio, video, text, or photos, and securely transfer it through wireless networks. In this paper, we analyse the performance characteristics of one of the steganography techniques called Hidden Communication System for Corrupted Networks (HCCNETs) for hiding sensitive data. This performance analysis includes the efficiency and the cost of the system in Wireless Local Area Networks (WLANs), specifically in the IEEE 802.11ah IoT protocol. The analysis is mainly based on a two-dimensional Markov chain model in the presence of an error channel. Additionally, the model considers packet arrival rate, back-off timer freezing, back-off stages, and short retry limit to ensure compliance with IEEE 802.11ah requirements. It stresses the importance of taking these elements into consideration while modeling the efficiency and cost of the steganographic channel system. These parameters often result in a high precise channel access estimation, a more accurate and efficient accuracy measurements system, efficient channel utilisation, avoidance of throughput saturation overestimation, and ensuring that no packet is served endlessly. Evaluated results demonstrate that HCCNETs is an effective approach at low cost.

Blockchain-based group key agreement protocol for vehicular ad hoc networks

Vehicular ad hoc network (VANET) has been attracting widespread attention because of its ability to improve traffic efficiency and safety. Since insecure wireless communication channels face various security threats (eavesdropping, tampering, forgery attack, etc.), many group key agreement protocols have been proposed for VANET. However, most of them rely on a trusted authority (TA) to perform authentication or group key generation. Excessive dependence on TA may lead to a single point of failure. Therefore, this paper designs a decentralized group key agreement protocol to solve this problem. TA is not involved in authentication or key agreement process after user registration. With blockchain technology, rode-side unit (RSU) and on-board units (OBUs) within its communication range can negotiate a group key without private information leakage. Moreover, the proposed protocol supports dynamic management (verification, update, and revocation) of vehicles’ public keys. Formal security proof demonstrates that the proposed protocol meets basic security requirements. Besides, performance evaluation and comparison show that the proposed protocol requires lower computation costs and communication overheads than some existing protocols.

A Blockchain-Based CP-ABE Scheme with Partially Hidden Access Structures

Data sharing has become a key technology to break down data silos in the big data era. Ciphertext-policy attribute-based encryption (CP-ABE) is widely used in secure data-sharing schemes to realize flexible and fine-grained access control. However, in traditional CP-ABE schemes, the access structure is directly shared along with the ciphertext, potentially leading to users’ private information leakage. Outsourcing data to a centralized third party can easily result in privacy leakage and single-point bottlenecks, and the lack of transparency in data storage and sharing casts doubts whether users’ data are safe. To address these issues, we propose a blockchain-based CP-ABE scheme with partially hidden access structures (BCP-ABE-PHAS) to achieve fine-grained access control while ensuring user privacy. First, we propose an efficient CP-ABE scheme with partially hidden access structures, where the ciphertext size is constant. To assist data decryption, we design a garbled Bloom filter to help users quickly locate the position of wildcards in the access structure. Then, to improve storage efficiency and system scalability, we propose a data storage scheme that combines blockchain technology and the interplanetary file system, ensuring data integrity. Finally, we employ smart contracts for a transparent data storage and sharing process without third-party participation. Security analysis and performance evaluation show that the proposed BCP-ABE-PHAS scheme can preserve policy privacy with efficient storage and low computational overhead.

Deep Learning-Based Object Tracking via Compressed Domain Residual Frames

The extensive rise of high-definition CCTV camera footage has stimulated both the data compression and the data analysis research fields. The increased awareness of citizens to the vulnerability of their private information, creates a third challenge for the video surveillance community that also has to encompass privacy protection. In this paper, we aim to tackle those needs by proposing a deep learning-based object tracking solution via compressed domain residual frames. The goal is to be able to provide a public and privacy-friendly image representation for data analysis. In this work, we explore a scenario where the tracking is achieved directly on a restricted part of the information extracted from the compressed domain. We utilize exclusively the residual frames already generated by the video compression codec to train and test our network. This very compact representation also acts as an information filter, which limits the amount of private information leakage in a video stream. We manage to show that using residual frames for deep learning-based object tracking can be just as effective as using classical decoded frames. More precisely, the use of residual frames is particularly beneficial in simple video surveillance scenarios with non-overlapping and continuous traffic.

Lightweight-blockchain based privacy-preserving data aggregation for epidemic disease surveillance

As the COVID-19 pandemic is raging worldwide, epidemic surveillance helps prevent the spread of the epidemic. Protecting the privacy of patients and data providers in the monitoring system can relieve them of their concerns about the leakage of private information, thereby improving the epidemic data collection capabilities of the system. In this article, we propose a lightweight-blockchain based privacy-preserving data aggregation scheme (LBPDA) for epidemic disease surveillance, which can aggregate data without relying on a trusted third party. Especially, to protect data privacy in the data aggregation process, the case count data is aggregated based on the Paillier cryptosystem's homomorphism. Besides, to reduce the time and storage overhead, we improved the adopted Hyperledger Fabric, thus lightening the data aggregation process. Finally, we simulated the proposed scheme and analyzed its security and performance to verify its feasibility and effectiveness. The results showed that the proposed scheme could meet the government department's requirements to aggregate patient case count data for epidemic disease surveillance while protecting the privacy of patients and data providers. Moreover, we also proved that the lightweight of blockchain is effective by comparison.

CoMSeC++: PUF-based secured light-weight mutual authentication protocol for Drone-enabled WSN

An unattended environment like a disputed area, a battlefield, or a dense forest area is an environment that is completely inaccessible. Hence, certain kinds of activities that may occur in such an environment would be difficult to notice, which actually is required to be known. Therefore, sensors could be mounted in those places so that the essential data could be gathered. In this paper, we are considering an unattended environment with sensors mounted at certain spots from which the data could be collected through mobile device which actually is an Unmanned Aerial Vehicle (UAV) or say, Drone. We have first designed a suitable architecture for the same and designed a light-weight protocol to establish a secure communication among devices and the cloud via the relocatable drone. Our protocol also utilizes the benefits of Physically Unclonable Function (PUF) to generate, which is used to encrypt the message in communication. The well-popular Scyther simulator has been used to simulate the protocol and the results shows that the protocol is completely protected against leakage of private information. Considering the hardness problem of hash function and PUF, the informal analysis also confirms the high-level security protection against known attacks. The protocol is not only better in terms of security strength, but it also achieves better performance when compared to existing competitive works.

A provably secure ECC-based access and handover authentication protocol for space information networks

At present, space information networks are widely used in various fields and gradually attract more and more users to access. However, because the wireless channel in space information networks is public, it is extremely vulnerable to attacks such as eavesdropping, replay and impersonation attacks, which may lead to the leakage of privacy and secret information. In recent years, many authentication schemes have been proposed to prevent malicious nodes from accessing and stealing resources, but most of these schemes either do not take into account the security of satellite relay nodes or the handover problems caused by dynamic topology. To solve the above-mentioned problems, we propose a novel ECC-based authentication scheme, which not only confirms the validity of the satellite node when user and ground station authenticate each other, but also designs the handover schemes based on two possible signal handover scenarios. For the handover involving the ground station, we also design an efficient multi-user batch handover authentication scheme, which can greatly reduce the computation overhead and delay. In the security analysis, we prove the security of our protocol based on the ROM model and AVISPA formal verification tool, and verify that our protocol can meet the security attributes and resist various forms of attacks through informal security analysis. Finally, security and performance analysis results demonstrate that our protocol is more suitable for user access and handover in space information networks while ensuring higher security requirements.

Privacy-Preserving Reversible Data Hiding for Medical Images Employing Local Rotation.

As digitalization becomes more common, patients' concerns about the leakage of private information, such as electronic medical record, are increasing, and those concerns motivated this case study of secure covert communication. Therefore, in this paper, a novel reversible data hiding method based on pixel rotation is proposed for medical images. Using pixel rotation, a state mapping model is constructed to represent the payload. More specifically, many intermediate states are derived from an image block, and each of them is used to form a one-to-one mapping relationship with a specific sequence of payload bits. In addition, to ensure the visual quality of stego-medical-images, the payload bits are only concealed in the regular blocks and the other blocks are unchanged. Moreover, the smoother regular image block will be priority to be used to embed the payload to enhance the visual quality of stego-medical-image. The experimental results showed that the stego-medical-images generated by the proposed reversible data hiding method have better visual quality with an average PSNR of 47.0307 dB, which is higher than that provided by some state-of-the-art methods.