An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

Protecting privacy of users in brain-computer interface applications

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving(PP) fashion, i.e., such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations

Cancellable Deep Learning Framework for EEG Biometrics

EEG-based biometric systems verify the identity of a user by comparing the probe to a reference EEG template of the claimed user enrolled in the system, or by classifying the probe against a user verification model stored in the system. These approaches are often referred to as template-based and model-based methods, respectively. Compared with template-based methods, model-based methods, especially those based on deep learning models, tend to provide enhanced performance and more flexible applications. However, there is no public research report on the security and cancellability issue for model-based approaches. This becomes a critical issue considering the growing popularity of deep learning in EEG biometric applications. In this study, we investigate the security issue of deep learning model-based EEG biometric systems, and demonstrate that model inversion attacks post a threat for such model-based systems. That is to say, an adversary can produce synthetic data based on the output and parameters of the user verification model to gain unauthorized access by the system. We propose a cancellable deep learning framework to defend against such attacks and protect system security. The framework utilizes a generative adversarial network to approximate a non-invertible transformation whose parameters can be changed to produce different data distributions. A user verification model is then trained using output generated from the generator model, while information about the transformation is discarded. The proposed framework is able to revoke compromised models to defend against hill climbing attacks and model inversion attacks. Evaluation results show that the proposed method, while being cancellable, achieves better verification performance than the template-based methods and state-of-the-art non-cancellable deep learning methods

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors

Classifying brain signals collected by wearable Internet of Things (IoT) sensors, especially brain-computer interfaces (BCIs), is one of the fastest-growing areas of research. However, research has mostly ignored the secure storage and privacy protection issues of collected personal neurophysiological data. Therefore, in this article, we try to bridge this gap and propose a secure privacy-preserving protocol for implementing BCI applications. We first transformed brain signals into images and used generative adversarial network to generate synthetic signals to protect data privacy. Subsequently, we applied the paradigm of transfer learning for signal classification. The proposed method was evaluated by a case study and results indicate that real electroencephalogram data augmented with artificially generated samples provide superior classification performance. In addition, we proposed a blockchain-based scheme and developed a prototype on Ethereum, which aims to make storing, querying and sharing personal neurophysiological data and analysis reports secure and privacy-aware. The rights of three main transaction bodies - construction workers, BCI service providers and project managers - are described and the advantages of the proposed system are discussed. We believe this paper provides a well-rounded solution to safeguard private data against cyber-attacks, level the playing field for BCI application developers, and to the end improve professional well-being in the industry

PolyCosGraph:A Privacy-Preserving Cancelable EEG Biometric System

Recent findings confirm that biometric templates derived from electroencephalography (EEG) signals contain sensitive information about registered users, such as age, gender, cognitive ability, mental status and health information. Existing privacy-preserving methods such as hash function and fuzzy commitment are not cancelable, where raw biometric features are vulnerable to hill-climbing attacks. To address this issue, we propose the PolyCosGraph, a system based on Polynomial transformation embedding Cosine functions with Graph features of EEG signals, which is a privacy-preserving and cancelable template design that protects EEG features and system security against multiple attacks. In addition, a template corrupting process is designed to further enhance the security of the system, and a corresponding matching algorithm is developed. Even when the transformed template is compromised, attackers cannot retrieve raw EEG features and the compromised template can be revoked. The proposed system achieves the authentication performance of 1.49% EER with a resting state protocol, 0.68% EER with a motor imagery task, and 0.46% EER under a watching movie condition, which is equivalent to that in the non-encrypted domain. Security analysis demonstrates that our system is resistant to attacks via record multiplicity, preimage attacks, hill-climbing attacks, second attacks and brute force attacks.</p

Assessing the Effectiveness of Automated Emotion Recognition in Adults and Children for Clinical Investigation

Recent success stories in automated object or face recognition, partly fuelled by deep learning artificial neural network (ANN) architectures, has led to the advancement of biometric research platforms and, to some extent, the resurrection of Artificial Intelligence (AI). In line with this general trend, inter-disciplinary approaches have taken place to automate the recognition of emotions in adults or children for the benefit of various applications such as identification of children emotions prior to a clinical investigation. Within this context, it turns out that automating emotion recognition is far from being straight forward with several challenges arising for both science(e.g., methodology underpinned by psychology) and technology (e.g., iMotions biometric research platform). In this paper, we present a methodology, experiment and interesting findings, which raise the following research questions for the recognition of emotions and attention in humans: a) adequacy of well-established techniques such as the International Affective Picture System (IAPS), b) adequacy of state-of-the-art biometric research platforms, c) the extent to which emotional responses may be different among children or adults. Our findings and first attempts to answer some of these research questions, are all based on a mixed sample of adults and children, who took part in the experiment resulting into a statistical analysis of numerous variables. These are related with, both automatically and interactively, captured responses of participants to a sample of IAPS pictures

Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018