Privacy-sensitive Information Research Articles

Federated Meta-Learning with Attention for Diversity-Aware Human Activity Recognition.

The ubiquity of smartphones equipped with multiple sensors has provided the possibility of automatically recognizing of human activity, which can benefit intelligent applications such as smart homes, health monitoring, and aging care. However, there are two major barriers to deploying an activity recognition model in real-world scenarios. Firstly, deep learning models for activity recognition use a large amount of sensor data, which are privacy-sensitive and hence cannot be shared or uploaded to a centralized server. Secondly, divergence in the distribution of sensory data exists among multiple individuals due to their diverse behavioral patterns and lifestyles, which contributes to difficulty in recognizing activity for large-scale users or 'cold-starts' for new users. To address these problems, we propose DivAR, a diversity-aware activity recognition framework based on a federated Meta-Learning architecture, which can extract general sensory features shared among individuals by a centralized embedding network and individual-specific features by attention module in each decentralized network. Specifically, we first classify individuals into multiple clusters according to their behavioral patterns and social factors. We then apply meta-learning in the architecture of federated learning, where a centralized meta-model learns common feature representations that can be transferred across all clusters of individuals, and multiple decentralized cluster-specific models are utilized to learn cluster-specific features. For each cluster-specific model, a CNN-based attention module learns cluster-specific features from the global model. In this way, by training with sensory data locally, privacy-sensitive information existing in sensory data can be preserved. To evaluate the model, we conduct two data collection experiments by collecting sensor readings from naturally used smartphones annotated with activity information in the real-life environment and constructing two multi-individual heterogeneous datasets. In addition, social characteristics including personality, mental health state, and behavior patterns are surveyed using questionnaires. Finally, extensive empirical results demonstrate that the proposed diversity-aware activity recognition model has a relatively better generalization ability and achieves competitive performance on multi-individual activity recognition tasks.

A Lightweight Three‐Party Mutual Authentication Protocol for Internet of Health Things Systems

In Internet of Health Things (IoHT) systems, there is a two‐hop network structure between the authentication server TA, Internet of Things Connector (IotC), and wearable sensor (WS). Attackers can use the sensor layer network (the first hop) between the IotC and WS to steal patient’s health‐related information and undermine the security of the system and the privacy of sensitive information. To address this threat, this study proposes a lightweight identity authentication and key agreement protocol for third‐party authentication servers TA, IotC, and WS. The results of the formal security proof, BAN logic analysis, and AVISPA tool simulation show that the scheme proposed in this study has an ideal security performance and can meet the security requirements of IoHT. In terms of performance, the proposed scheme could dynamically construct a sensor layer network (the first hop) and offline networking according to the diagnostic needs of doctors. Compared with other related protocols, the proposed scheme can significantly reduce the computing resource requirements of IotC and server TA and the resource requirements of database I/O operation of server TA in the application scenario of concurrent access of multiple WS nodes.

Ensuring Brand Safety and Reputation in Digital Marketing with Advanced Cybersecurity Protocols

The ever-changing world of digital marketing makes it more important than ever to protect the integrity of brands. This study presents a novel method called "Enhanced Brand Safety Assurance through Cybersecurity Protocols" that combines three important algorithms: Ad Fraud Detection and Prevention, Real-time Behavioral Analysis, and Threat Intelligence Integration. The security of digital advertising, privacy of sensitive information, and customer confidence may all be assured with this framework's proactive threat detection and mitigation capabilities. A strong protection system against ever-changing cyber threats is created by combining the unique characteristics of each algorithm. To react to the constantly changing cybersecurity scene, the suggested solution uses adaptive thresholds, machine learning, and sophisticated analytics. When compared to more conventional approaches, the suggested solution outperforms them in terms of important efficacy indicators and practical implementation details. Experiments show that it can learn a lot, integrate AI, adapt to threats, monitor in real-time, and identify threats very well. Brands can protect themselves from the complex digital threat environment with this comprehensive and proactive cybersecurity solution that tackles the many problems associated with digital marketing.

Understanding Person Identification Through Gait

Gait recognition is the process of identifying humans from their bipedal locomotion such as walking or running. As such, gait data is privacy sensitive information and should be anonymized where possible. With the rise of higher quality gait recording techniques, such as depth cameras or motion capture suits, an increasing amount of detailed gait data is captured and processed. The introduction and rise of the Metaverse is an example of a potentially popular application scenario in which the gait of users is transferred onto digital avatars. As a first step towards developing effective anonymization techniques for high-quality gait data, we study different aspects of movement data to quantify their contribution to gait recognition. We first extract categories of features from the literature on human gait perception and then design experiments for each category to assess how much the information they contain contributes to recognition success. We evaluated the utility of gait perturbation by means of naturalness ratings in a user study. Our results show that gait anonymization will be challenging, as the data is highly redundant and inter-dependent.

Covertvasion: Depicting threats through covert channels based novel evasive attacks in android

Privacy and security issues concerning mobile devices have substantial consequences for individuals, groups, governments, and businesses. The Android operating system bolsters smartphone data protection by imposing restrictions on app behavior. Nevertheless, attackers conduct systematic resource analyses and divert privacy-sensitive information from plain view. They employ evasive mechanisms to evade system monitoring and create an illusion of benign and non-sensitive communication. Furthermore, covert channels amplify the impact of these malicious activities by facilitating information transfer through non-standard methods. The purpose of this research is to shed light on these novel threats targeting Android systems. The study delves into security and privacy attacks that compromise sensitive user information. The methodology leverages evasion concepts and employs sound-specific covert channel communication, particularly ultrasonic channels. This research work introduces novel evasive attacks, namely Prime-Composite Evasive Information Invasion (PCEII) and File-lock-based Evasive Information Invasion (FEII), both relying on covert channel communication. These unique variants of attacks successfully evade user data within a few milliseconds for both noisy as well as non-noisy environments and do not show any signs of detection by antivirus mechanisms like Anti-Virus Guard (AVG), 360 security, etc. and state-of-the-art tools such as TaintDroid, MockDroid and others. The paper not only assesses their impact on the privacy and security of information but also introduces avenues for their detection and mitigation.

Gaussian Mechanism Design for Prescribed Privacy Sets in Data Releasing Systems

The data transmitted by cyber-physical systems can be intercepted and exploited by malicious individuals to infer privacy-sensitive information regarding the physical system. This motivates us to study the problem of preserving privacy in data releasing of linear dynamical system using stochastic perturbation. In this study, the privacy sensitive quantity is the initial state value of the system where for protecting its privacy, we directly design the covariance matrix of a Gaussian output noise to achieve a prescribed uncertainty set in the form of hyper-ellipsoids. This is done by correlated noise and through a convex optimization problem by considering the utility of released signals. Compared to other available methods, our proposed technique for designing the Gaussian output noise provides enhanced flexibility for system designers. As a case study, the results are applied to a heating ventilation and air conditioning system.

Intelligent Mobile Edge Computing Integrated with Blockchain Security Analysis for Millimetre-Wave Communication

With the increase in number of devices enabled the Internet of Things (IoT) communication with the centralized cloud computing model. With the implementation of the cloud computing model leads to increased Quality of Service (QoS). The cloud computing model provides the edge computing technologies for the real-time application to achieve reliability and security. Edge computing is considered the extension of the cloud computing technology involved in transfer of the sensitive information in the cloud edge to increase the network security. The real-time data transmission realizes the interaction with the high frequency to derive improved network security. However, with edge computing server security is considered as sensitive privacy information maintenance. The information generated from the IoT devices are separated based on stored edge servers based on the service location. Edge computing data is separated based in edge servers for the guaranteed data integrity for the data loss and storage. Blockchain technologies are subjected to different security problem for the data integrity through integrated blockchain technologies. This paper developed a Voted Blockchain Elliptical Curve Cryptography (VBECC) model for the millimetre wave application. The examination of the blockchain model is evaluated based on the edge computing architecture. The VBECC model develop an architectural model based Blockchain technology with the voting scheme for the millimetre application. The estimated voting scheme computes the edge computing technologies for the estimation of features through ECC model. The VBECC model computes the security model for the data transmission in the edge computing-based millimetre application. The experimental analysis stated that VBECC model uses the data security model ~8% increased performance than the conventional technique.

Provably secure certificateless protocol for wireless body area network

Wireless body area networks are gaining popularity due to their innovative applications such as timely analysis, remote monitoring of patients’ health, and high patient care quality. However, these healthcare systems that carry patient’s physiological data need special attention for the security and privacy of information. Due to the openness of transmitted data, the healthcare system gets prone to several adverse attacks. In this paper, a provably secure remote healthcare system is proposed based on the elliptic curve cryptosystem. The goal is to enable confidentiality and privacy of sensitive information by designing a certificateless authenticated key agreement protocol with low computational cost and higher security. The proposed scheme achieves anonymity, resistance to key escrow problems, mutual authentication between the sensor nodes attached to patients and the application provider. Furthermore, the protocol undergoes formal security analysis using the random oracle model, and the soundness of the proposed scheme is validated using ProVerif. Finally, the performance analysis depicts that the proposed scheme is efficient compared to existing methods.

Understanding Human Reactions Looking at Facial Microexpressions With an Event Camera

With the establishment of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Industry 4.0</i> , machines are now required to interact with workers. By observing biometrics they can assess if humans are authorized, or mentally and physically fit to work. Understanding body language, makes human–machine interaction more natural, secure, and effective. Nonetheless, traditional cameras have limitations; low frame rate and dynamic range hinder a comprehensive human understanding. This poses a challenge, since faces undergo frequent instantaneous microexpressions. In addition, this is privacy-sensitive information that must be protected. We propose to model expressions with event cameras, bio-inspired vision sensors that have found application within the Industry 4.0 scope. They capture motion at millisecond rates and work under challenging conditions like low illumination and highly dynamic scenes. Such cameras are also privacy-preserving, making them extremely interesting for industry. We show that using event cameras, we can understand human reactions by only observing facial expressions. Comparison with red-green-blue (RGB)-based modeling demonstrates improved effectiveness and robustness.

Generating evidence on privacy outcomes to inform privacy risk management: A way forward?

Effective and efficient privacy risk management (PRM) is a necessary condition to support digitalization in health care and secondary use of patient data in research. To reduce privacy risks, current PRM frameworks are rooted in an approach trying to reduce undesired technical/organizational outcomes such as broken encryption or unintentional data disclosure. Comparing this with risk management in preventive or therapeutic medicine, a key difference becomes apparent: in health-related risk management, medicine focuses on person-specific health outcomes, whereas PRM mostly targets more indirect, technical/organizational outcomes. In this paper, we illustrate and discuss how a PRM approach based on evidence of person-specific privacy outcomes might look using three consecutive steps: i) a specification of undesired person-specific privacy outcomes, ii) empirical assessments of their frequency and severity, and iii) empirical studies on how effectively the available PRM interventions reduce their frequency or severity. After an introduction of these three steps, we cover their status quo and outline open questions and PRM-specific challenges in need of further conceptual clarification and feasibility studies. Specific challenges of an outcome-oriented approach to PRM include the potential delays between concrete threats manifesting and the resulting person/group-specific privacy outcomes. Moreover, new ways of exploiting privacy-sensitive information to harm individuals could be developed in the future. The challenges described are of technical, legal, ethical, financial and resource-oriented nature. In health research, however, there is explicit discussion about how to overcome such challenges to make important outcome-based assessments as feasible as possible. This paper concludes that it might be the time to have this discussion in the PRM field as well.

An Energy-efficient and Privacy-aware Decomposition Framework for Edge-assisted Federated Learning

Deep Learning (DL) is an essential technology for modern intelligent sensor network and interactive multimedia applications, having problems with user data privacy when training on a central cloud. While Federated Learning (FL) motivates to preserve user privacy, it also causes new problems of lower user terminal usability and training efficiency, which caused substantial energy consumption. This article proposes a novel energy-efficient and privacy-aware decomposition framework to improve user-side FL efficiency under pre-defined privacy requirements with the assistance of Mobile Edge Computing (MEC) and Software Decomposition. It takes the propagation of each neural layer as the migrating unit and considers the tradeoff relationship between privacy and efficiency. We also propose an online scheduling algorithm to optimize the framework’s training performance. Furthermore, we summarize eight privacy-sensitive information classes on which existing privacy attacks base and design configurable privacy preservation mechanisms for each class. Simulations and experiments prove the effectiveness of our framework and algorithm in FL efficiency improvement and the effects of different privacy constraints on the overall training efficiency.

Automated Detection of Doxing on Twitter

Doxing refers to the practice of disclosing sensitive personal information about a person without their consent. This form of cyberbullying is an unpleasant and sometimes dangerous phenomenon for online social networks. Although prior work exists on automated identification of other types of cyberbullying, a need exists for methods capable of detecting doxing on Twitter specifically. We propose and evaluate a set of approaches for automatically detecting second- and third-party disclosures on Twitter of sensitive private information, a subset of which constitutes doxing. We summarize our findings of common intentions behind doxing episodes and compare nine different approaches for automated detection based on string-matching and one-hot encoded heuristics, as well as word and contextualized string embedding representations of tweets. We identify an approach providing 96.86% accuracy and 97.37% recall using contextualized string embeddings and conclude by discussing the practicality of our proposed methods.

Machine Learning on Cloud With Blockchain: A Secure, Verifiable and Fair Approach to Outsource the Linear Regression

Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work, and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.

A blockchain-based fine-grained data sharing scheme for e-healthcare system

The cloud-aided sharing of e-healthcare data has great positive significance for research. However, due to the privacy consideration, these data are usually encrypted before uploading to the cloud server which impedes data sharing between different medical institutions. Conditional proxy re-encryption (CPRE) allows the proxy to converse ciphertext, especially by specifying a condition embed in the re-encryption key to achieve fine-grained access control over the ciphertext. Unfortunately, existing CPRE schemes cannot ensure the privacy of the condition, which may contain some sensitive private information. Furthermore, a malicious proxy server may return part of the results and even false data to save its computation or bandwidth. To solve these problems, we propose a blockchain-based condition invisible proxy re-encryption scheme for the e-healthcare system. The proposed scheme guarantees the confidentiality of the data by hiding the condition in the re-encryption key so that the proxy cannot learn anything about the condition. Moreover, the ciphertext-searching algorithm is leveraged by executing the smart contract in the blockchain which ensures the results are correct and complete. Finally, experiment results demonstrate the practicability of the proposed scheme in applications.

GRASCCO - The First Publicly Shareable, Multiply-Alienated German Clinical Text Corpus.

We describe the creation of GRASCCO, a novel German-language corpus composed of some 60 clinical documents with more than.43,000 tokens. GRASCCO is a synthetic corpus resulting from a series of alienation steps to obfuscate privacy-sensitive information contained in real clinical documents, the true origin of all GRASCCO texts. Therefore, it is publicly shareable without any legal restrictions We also explore whether this corpus still represents common clinical language use by comparison with a real (non-shareable) clinical corpus we developed as a contribution to the Medical Informatics Initiative in Germany (MII) within the SMITH consortium. We find evidence that such a claim can indeed be made.

A federated learning method for real-time emotion state classification from multi-modal streaming.

Emotional and physical health are strongly connected and should be taken care of simultaneously to ensure completely healthy persons. A person's emotional health can be determined by detecting emotional states from various physiological measurements (EDA, RB, EEG, etc.). Affective Computing has become the field of interest, which uses software and hardware to detect emotional states. In the IoT era, wearable sensor-based real-time multi-modal emotion state classification has become one of the hottest topics. In such setting, a data stream is generated from wearable-sensor devices, data accessibility is restricted to those devices only and usually a high data generation rate should be processed to achieve real-time emotion state responses. Additionally, protecting the users' data privacy makes the processing of such data even more challenging. Traditional classifiers have limitations to achieve high accuracy of emotional state detection under demanding requirements of decentralized data and protecting users' privacy of sensitive information as such classifiers need to see all data. Here comes the federated learning, whose main idea is to create a global classifier without accessing the users' local data. Therefore, we have developed a federated learning framework for real-time emotion state classification using multi-modal physiological data streams from wearable sensors, called Fed-ReMECS. The main findings of our Fed-ReMECS framework are the development of an efficient and scalable real-time emotion classification system from distributed multimodal physiological data streams, where the global classifier is built without accessing (privacy protection) the users' data in an IoT environment. The experimental study is conducted using the popularly used multi-modal benchmark DEAP dataset for emotion classification. The results show the effectiveness of our developed approach in terms of accuracy, efficiency, scalability and users' data privacy protection.

Comparative Analysis and Development of Mobile Device Authentication Framework for Corporate Networks

Several systematic reviews on mobile device technologies have been undertaken mostly identifying mobile security threats and challenges to corporate organisations' sensitive private information. This paper surveyed the existing level of secure authentication achieved by various mobile device-related frameworks against their listed goals. The solutions and security level of the existing authentication approaches among these categories were compared and improved on the KANYI BYOND framework by introducing a Radius server with the 802.11 authentications supported feature that provides access control to wireless routers, access points, hotspots in EAP/WPA-Enterprise/WPA2-Enterprise modes as means to achieve multiple authentications to mobile device users in corporate networks. Testing and validation of the resulting framework were done with the help of a riverbed modeler and a Denial of Service attack was simulated on all mobile devices' nodes in the designed network. The results indicated that the resulting framework provides multiple authentications and is thought to overcome self-reassuring by mobile device users on the network.

Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy

With the popularization and development of social software, more and more people join the social network, which produces a lot of valuable information, but also contains plenty of sensitive privacy information. To achieve the personalized privacy protection of massive social network relational data, a privacy enhancement method for social networks relational data based on personalized differential privacy is proposed. And a dimensionality reduction segmentation sampling (DRS-S) algorithm is proposed to implement this method. First, in order to solve the problem of inefficiency caused by the excessive amount of data in social networks, dimension reduction and segmentation are carried out to divide the data into groups. According to the privacy protection requirements of different users, we adopt sampling method to protect users with different privacy requirements at different levels, so as to realize personalized different privacy. After that, the noise is added to the protected data to satisfy the privacy budget. Then publish the social network data. Finally, the proposed algorithm is compared with the traditional personalized differential privacy (PDP) algorithm and privacy preserving approach based on clustering and noise (PBCN) in real data set, the experimental results demonstrate that the quality of privacy protection and data availability of DRS-S are better than that of PDP algorithm and PBCN algorithm.

Differential Private Deep Learning Models for Analyzing Breast Cancer Omics Data.

Proper analysis of high-dimensional human genomic data is necessary to increase human knowledge about fundamental biological questions such as disease associations and drug sensitivity. However, such data contain sensitive private information about individuals and can be used to identify an individual (i.e., privacy violation) uniquely. Therefore, raw genomic datasets cannot be publicly published or shared with researchers. The recent success of deep learning (DL) in diverse problems proved its suitability for analyzing the high volume of high-dimensional genomic data. Still, DL-based models leak information about the training samples. To overcome this challenge, we can incorporate differential privacy mechanisms into the DL analysis framework as differential privacy can protect individuals’ privacy. We proposed a differential privacy based DL framework to solve two biological problems: breast cancer status (BCS) and cancer type (CT) classification, and drug sensitivity prediction. To predict BCS and CT using genomic data, we built a differential private (DP) deep autoencoder (dpAE) using private gene expression datasets that performs low-dimensional data representation learning. We used dpAE features to build multiple DP binary classifiers to predict BCS and CT in any individual. To predict drug sensitivity, we used the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. We extracted GDSC’s dpAE features to build our DP drug sensitivity prediction model for 265 drugs. Evaluation of our proposed DP framework shows that it achieves improved prediction performance in predicting BCS, CT, and drug sensitivity than the previously published DP work.

MID-Crypt: A Cryptographic Algorithm for Advanced Medical Images Protection

Privacy-preserving of medical information (such as medical records and images) is an essential right for patients to ensure security against undesired access parties. This right is typically protected by law through firm regulations set by healthcare authorities. However, sensitive-private data usually requires the application of further security and privacy mechanisms such as encipherment (encryption) techniques. ’Medical images’ is one such example of highly demanding security and privacy standards. This is due to the quality and nature of the information carried among these images, which are usually sensitive-private information with few features and tonal variety. Hence, several state-of-the-art encryption mechanisms for medical images have been proposed and developed; however, only a few were efficient and promising. This paper presents a hybrid crypto-algorithm, MID-Crypt, to secure the medical image communicated between medical laboratories and doctors’ accounts. MID-Crypt is designed to efficiently hide medical image features and provide high-security standards. Specifically, MID-Crypt uses a mix of Elliptic-curve Diffie–Hellman (ECDH) for image masking and Advanced Encryption Standard (AES) with updatable keys for image encryption. Besides, a key management module is used to organize the public and private keys, the patient’s digital signature provides authenticity, and integrity is guaranteed by using the Merkle tree. Also, we evaluated our proposed algorithm in terms of several performance indicators including, peak signal-to-noise ratio (PSNR) analysis, correlation analysis, entropy analysis, histogram analysis, and timing analysis. Consequently, our empirical results revealed the superiority of MID-Crypt scoring the best performance values for PSNR, correlation, entropy, and encryption overhead. Finally, we compared the security measures for the MID-Crypt algorithm with other studies, the comparison revealed the distinguishable security against several common attacks such as side-channel attacks (SCA), differential attacks, man-in-the-middle attacks (MITM), and algebraic attacks.