High-density surface electromyogram (HD-sEMG) has become a powerful signal source for hand gesture recognition. However, existing approaches suffer from limited feature diversity in hand-crafted methods and high data dependency in deep learning models, necessitating individual model calibration for each user due to neuromuscular differences. We propose EMG-ROCKET, an enhanced version of the RandOm Convolutional KErnel Transform (ROCKET), designed to extract diverse and robust HD-sEMG features without prior knowledge or extensive training. EMG-ROCKET integrates random channel fusion and enhanced aggregation functions to enhance robustness against cross-day signal variability in HD-sEMG applications. In cross-day evaluations of hand gesture recognition, a Ridge classifier using EMG-ROCKET features achieved 84.3% and 77.8% accuracy on two HD-sEMG datasets, outperforming all baseline methods. Furthermore, feature contribution analysis demonstrates the capability of EMG-ROCKET to capture spatial muscle activation patterns, offering insights into motion mechanisms. These results establish EMG-ROCKET as a promising, training-free solution for robust HD-sEMG feature extraction, facilitating practical human-machine interaction applications.

Surface electromyography (sEMG) -based human-computer interaction (HCI) systems achieve high accuracy in controlled environments, but their robustness under daily life remains challenging. In real-world scenarios, variations in user posture introduce personalized biases that can significantly degrade model performance. A viable solution is to train a highly generalized network using existing data from various postures, enabling the model to become less sensitive to posture variations. In this work, we treat the original sEMG signals as a coupling of pattern and posture components, where each component can be considered as a causal signal specific to corresponding labels. We use the causal encoders to understand the generative relationships between data and labels, facilitating the disentanglement of components into different latent spaces and promoting clustering within each space. This enables the model to extract posture-invariant pattern components and train a robust pattern recognition model with strong generalization capabilities. We developed a high-density sEMG (HD-sEMG) dataset with 16 subjects performing in four common HCI postures, addressing the lack of posture variation samples in existing sEMG datasets. Our model achieved an average accuracy of 90.3% across four generalization tasks, outperforming other domain generalization models and demonstrating its superiority.

Electroencephalography (EEG) is a widely used physiological signal to obtain information of brain activity, and its automatic detection holds significant research importance, which saves doctors' time, improves detection efficiency and accuracy. However, current automatic detection studies face several challenges: large EEG data volumes require substantial time and space for data reading and model training; EEG's long-term dependencies test the temporal feature extraction capabilities of models; and the dynamic changes in brain activity and the non-Euclidean spatial structure between electrodes complicate the acquisition of spatial information. The proposed method uses range-EEG (rEEG) to extract time-frequency features from EEG to reduce data volume and resource consumption. Additionally, the next-generation state-space model Mamba is utilized as a temporal feature extractor to effectively capture the temporal information in EEG data. To address the limitations of state space models (SSMs) in spatial feature extraction, Mamba is combined with Dynamic Graph Neural Networks, creating an efficient model called DG-Mamba for EEG event detection. Testing on seizure detection and sleep stage classification tasks showed that the proposed method improved training speed by 10 times and reduced memory usage to less than one-seventh of the original data while maintaining superior performance. On the TUSZ dataset, DG-Mamba achieved an AUROC of 0.931 for seizure detection and in the sleep stage classification task, the proposed model surpassed all baselines.

Surface electromyogram (sEMG) signals are widely used in human-machine interfaces for gesture recognition and user identification, but existing models often struggle with generalization across different individuals due to subject-specific neuromuscular characteristics. This study introduced a disentanglement model to separate task-specific and subject-specific components from sEMG signals, thus improving the generalization and interpretability of gesture recognition and user identification systems. Experimental results demonstrate that disentangled task-specific components significantly improve the accuracy of both gesture classification and user identification across different subjects and days, outperforming conventional methods in the same scenario. Further analysis of the extracted components reveals that task-specific components capture consistent activation patterns for the same gestures across individuals. In contrast, subject-specific components reflect unique neuromuscular characteristics that can be used for user identification. Notably, subject-specific components show reduced similarity compared to task-specific components in inter-day scenarios, contributing to more accuracy decrease in user identification than in gesture recognition. These findings suggest that the disentanglement approach not only boosts classification performance but also provides deeper insights into the physiological mechanisms underlying sEMG signals. The model's ability to isolate and interpret different neuromuscular components holds promise for enhancing the robustness of sEMG-based applications in real-world settings, such as rehabilitation and user authentication.

High-density surface EMG (HD-sEMG) is gaining attention because of its non-invasive nature and high spatial resolution. However, wearable HD-sEMG measurements with over 128 channels face challenges in system integration and reliable network-free inter-device synchronization. This article presents a wireless distributed wearable HD-sEMG acquisition system named Oct-HD, which supports up to eight acquisition modules (512 channels) with microsecond-level synchronization without requiring a network. The full-channel impedance detection ensures reliable electrode-skin contact and signal acquisition. The system also includes a self-locking base station that stores, charges, and configures the modules. Both simulated and real-world validation demonstrate that the system maintains a long-term inter-module offline error within 3ms, even under vibrations and extreme temperatures, demonstrating reliability for network-free outdoor and open-space monitoring. To support high-level signal interpretation, we further developed an integrated analysis software suite alongside the hardware. This toolkit enables motor unit decomposition, feature extraction, root mean square (RMS) map and power spectral density (PSD) analysis. Comparative experiments with a commercial system (Sessantaquattro) involving ten hand postures and 17 subjects were conducted, as hand gesture recognition is one of the most common applications in the field of electromyography. Results showed a significant performance improvement (p<10-5) of Oct-HD over the state-of-the-art system in signal quality and anti-interference capacity. Gesture classification results across four mainstream models demonstrated general accuracy improvements with Oct-HD over the commercial system in both dynamic and maintenance tasks. This highlights the effectiveness of Oct-HD in enhancing applications in prosthetic control and human-computer interaction. The Oct-HD system offers a notable advancement in wireless HD-sEMG acquisition, offering superior channel capacity, synchronization precision, signal quality, and anti-interference capacity compared to existing systems. The network-free microsecond-level synchronization and enhanced signal performance provide greater monitoring flexibility across various muscle groups, paving the way for broader applications in human-machine interaction.

Electromyography (EMG) with individually unique characteristics, has emerged as a promising biometric trait. The capability to further encrypt EMG biometric patterns via distinct muscle activities (serve as a password), characterizes EMG biometrics with both a high recognition accuracy and revocability. The biometric component and the password component together form the global patterns of EMG. Previous EMG biometric verification methods directly extracted features from EMG signals to form global EMG representations with the biometric and password components entangled together. In this work, a disentanglement model was applied to disentangle the global EMG representations into password-specific and biometric-specific components in two separate latent spaces. The disentanglement model was built on a multibranch-encoder and single-decoder architecture. The two disentangled representations were learned separately by two cascaded support-vector domain description (SVDD) models. The model was trained and tested with data acquired on different days, to validate the interday robustness of our system, which is important for biometric verification using variable physiological signals. Results demonstrated that learning from disentangled representations contributes to a better EMG biometric verification performance compared with learning directly from the global representation. Our method achieved an Equal Error Rate (EER) of 0.0075 when impostors do not know the passwords. Furthermore, even when the impostors know the password, the biometric defense alone still managed to prevent intrusion with an EER of 0.1582. To the best of our knowledge, this is the first study to employ disentangled EMG representations for biometric verification.

Surface electromyography (sEMG)-based gesture recognition can achieve high intra-session performance. However, the inter-session performance of gesture recognition decreases sharply due to the shift in data distribution. Therefore, developing a robust model to minimize the data distribution difference is crucial to improving the user experience. In this work, based on the inter-session gesture recognition task, we propose a novel algorithm called locality preserving and maximum margin criterion (LPMM). The LPMM algorithm integrates three main modules, including domain alignment, pseudo-label selection, and iteration result selection. Domain alignment is designed to preserve the neighborhood structure of the feature and minimize the overlap of different classes. The pseudo-label selection and iteration result selection can avoid the decrease in accuracy caused by mislabeled samples. The proposed algorithm was evaluated on two of the most widely used EMG databases. It achieves a mean accuracy of 98.46% and 71.64%, respectively, which is superior to state-of-the-art domain adaptation methods.

Surface electromyogram (sEMG) has been widely used in hand gesture recognition. However, most previous studies focused on user-personalized models, which require a great amount of data from each new target user to learn the user-specific EMG patterns. In this work, we present a novel real-time gesture recognition framework based on multi-source domain adaptation, which learns extra knowledge from the data of other users, thereby reducing the data collection burdens on the target user. Additionally, compared with conventional domain adaptation methods which treat data from all users in the source domain as a whole, the proposed multi-source method treat data from different users as multiple separate source domains. Therefore, more detailed statistical information on the data distribution from each user can be learned effectively. High-density sEMG (256 channels) from 20 subjects was used to validate the proposed method. Importantly, we evaluated our method with a simulated real-time processing pipeline on continuous sEMG data stream, rather than well-segmented data. The false alarm rate during rest periods in an EMG data stream, which is typically neglected by previous studies performing offline analyses, was also considered. Our results showed that, with only 1 s sEMG data per gesture from the new user, the 10-gesture classification accuracy reached 87.66 % but the false alarm rate was reduced to 1.95 %. Our method can reduce the frustratingly heavy data collection burdens on each new user.

With massive data collected in Internet of Things (IoT)-based smart environment, improving privacy preservation via client verification and identification is crucial. Surface electromyogram (sEMG) has emerged as a cancelable neuromuscular biometric trait, which makes up the noncancelability flaw of the traditional face and fingerprint biometrics. Current studies are in the proof-of-concept stage. In-depth studies to find the optimal solution to decode sEMG biometrics with excellent cross-day performance are very scarce. For neurophysiological biometrics, the permanence across time is a crucial factor. Our work aims to optimize the cross-day performance of the sEMG biometric decoder. We systematically evaluated the performance of 28 hand gestures to generate sEMG, 55 temporal–spectral–spatial features to represent sEMG, 9 distance measures and 9 classifiers to make decisions. Both biometric verification and identification were investigated in rigorous cross-day validations. Results show that the optimal combination of ≥ 10 temporal–spectral–spatial features achieved the best cross-day performance with city-block distance and support vector machine (SVM) applied. EMG generated by middle finger extension and hand close is preferred as biometric tokens. Using the optimized decoder, a cross-day identification accuracy of 88.75% and verification error rate of 9.85% were achieved. The verification error rate could be further reduced to 2.45% if impostors input sEMG under random gestures. Moreover, our work proved the reliability of sEMG biometrics even under muscle fatigue for the first time. This is also the first study to systematically evaluate the cross-day performance of different components in sEMG biometric decoding systems, serving as a technique-screening tool for future studies.

Machine and deep learning techniques have received increasing attentions in estimating finger forces from high-density surface electromyography (HDsEMG), especially for neural interfacing. However, most machine learning models are normally employed as block-box modules. Additionally, most previous models suffer from performance degradation when dealing with noisy signals. In this work, we propose to employ a forest ensemble model for HDsEMG-force modeling. Our model is explainable and robust against noise. Additionally, we explored the effect of increasing the depth of forest models in EMG-force modeling problems. We evaluated the performance of deep forests with a finger force estimation task. Training and testing data were acquired 3-25 days apart, approximating realistic scenarios. Results showed that deep forests significantly outperformed other models. With artificial signal distortion in 20% channels, deep forests also showed a higher robustness, with the error reduced from that of the baseline by 50% compared with all other models. We provided explanations for the proposed model using the mean decrease impurity (MDI) metric, revealing a strong correspondence between the model and physiology.

Continuous estimation of finger joints based on surface electromyography (sEMG) has attracted much attention in the field of human-machine interface (HMI). A couple of deep learning models were proposed to estimate the finger joint angles for specific subject. When applied onto a new subject, however, the performance of the subject-specific model would degrade significantly due to the inter-subject differences. Therefore, a novel cross-subject generic (CSG) model was proposed in this study to estimate continuous kinematics of finger joints for new users. Firstly, a multi-subject model based on the LSTA-Conv network was built by using sEMG and finger joint angles data from multiple subjects. Then, the subjects adversarial knowledge (SAK) transfer learning strategy was adopted to calibrate the multi-subject model with the training data from a new user. With the updated model parameters and the testing data from the new user, multiple finger joint angles could be estimated afterwards. The overall performance of the CSG model for new users was validated on three public datasets from Ninapro. The results showed that the newly proposed CSG model significantly outperformed five subject-specific models and two transfer learning models in terms of Pearson correlation coefficient, root mean square error, and coefficient of determination. Comparison analysis showed that both the long short-term feature aggregation (LSTA) module and the SAK transfer learning strategy contributed to the CSG model. Moreover, increasing number of subjects in training set improved the generalization capability of the CSG model. The novel CSG model would facilitate the application of robotic hand control and other HMI settings.

Human–machine interaction requires accurate recognition of human intentions (e.g., via hand gestures). Here, we assessed the cross-day robustness of widely used hand gesture classification techniques applied to high-density surface electromyogram (HD-sEMG) signals (256 channels). Our evaluation covered techniques in each stage of the classification framework: first, 50 temporal-spectral-spatial domain features, second, 15 feature optimization techniques, and third, seven classifiers. Moreover, although HD-sEMG provides sufficient neuromuscular information, some of the channels may present low signal-to-noise ratio and should therefore be treated as outliers. Accordingly, we performed our evaluation with, first, all outlier channels retained, and second, removal of the features corresponding to poor-quality channels and substitution with interpolated values from neighbor channels. The impact of sliding window and data augmentation was also investigated. We examined the results on a 35-gesture classification task using HD-sEMG acquired from 20 subjects on two sessions in separate days. The results showed that interpolation of features from outlier channels significantly improved the performance in most cases. Use of a sliding window and of data augmentation contributed to a higher classification accuracy. For the classification of 11 selected gestures of common daily use, the support vector machine classifier achieved the highest classification accuracy of 91.9% in a cross-day validation protocol using an optimal combination of 13 features (each extracted from sliding windows), feature optimization by linear discriminant analysis, and data augmentation. Our work can serve as a technique-screening tool on cross-day applications of human–machine interactions.

Conventional biometric modalities, such as the face, fingerprint, and iris, are vulnerable against imitation and circumvention. Accordingly, secure biometric modalities with cancelable properties are needed for personal identification, especially in smart healthcare applications. Here we developed a person identification model using high-density surface electromyography (HD-sEMG) as biometric traits. In this model, the HD-sEMG biometric templates are cancelable and could be customized by the users through finger isometric contractions. A deep feature learning approach, implemented by convolutional neural networks (CNNs) is used to capture user-specific patterns from HD-sEMG signals and make identification decisions. This model has been validated on twenty-two subjects, with training and testing data acquired from two different days. The rank-1 identification accuracy and equal error rate for 44 identities (22 subjects × 2 accounts) can reach 87.23% and 4.66%, respectively. The cross-day identification accuracy of the proposed model is higher than the results of previous methods reported in the literature. The usability and efficiency of the proposed model are also investigated, indicating its potentials for practical applications.

Enhancing information security via reliable user authentication in wireless body area network (WBAN)-based Internet-of-Things (IoT) applications has attracted increasing attention. The noncancelability of traditional biometrics (e.g., fingerprint) for user authentication increases the privacy disclosure risks once the biometric template is exposed, because users cannot volitionally create a new template. In this work, we propose a cancelable biometric modality based on high-density surface electromyogram (HD-sEMG) encoded by hand gesture password, for user authentication. HD-sEMG signals (256 channels) were acquired from the forearm muscles when users performed a prescribed gesture password, forming their biometric token. Thirty four alternative hand gestures in common daily use were studied. Moreover, to reduce the data acquisition and transmission burden in IoT devices, an automatically generated password-specific channel mask was employed to reduce the number of active channels. HD-sEMG biometrics were also robust with reduced sampling rate, further reducing power consumption. HD-sEMG biometrics achieved a low equal error rate (EER) of 0.0013 when impostors entered a wrong gesture password, as validated on 20 subjects. Even if impostors entered the correct gesture password, the HD-sEMG biometrics still achieved an EER of 0.0273. If the HD-sEMG biometric template was exposed, users could cancel it by simply changing it to a new gesture password, with an EER of 0.0013. To the best of our knowledge, this is the first study to employ HD-sEMG signals under common daily hand gestures as biometric tokens, with training and testing data acquired on different days.

In this article, we propose a novel neuromuscular password-based user authentication method. The method consists of two parts: surface electromyogram (sEMG) based finger muscle isometric contraction password (FMICP) and neuromuscular biometrics. FMICP can be entered through isometric contraction of different finger muscles in a prescribed order without actual finger movement, which makes it difficult for observers to obtain the password. In our study, the isometric contraction patterns of different finger muscles were recognized through high-density sEMG signals acquired from the right dorsal hand. Moreover, both time-frequency-space domain features at macroscopic level (interference-pattern EMG) and motor neuron firing rate features at microscopic level (via decomposition) were extracted to represent neuromuscular biometrics, serving as a second defense. The FMICP and macro-micro neuromuscular biometrics together form a neuromuscular password. The proposed neuromuscular password achieved an equal error rate (EER) of 0.0128 when impostors entered a wrong FMICP. Even when impostors entered the correct FMICP, the neuromuscular biometrics, as the second defense, inhibited impostors with an EER of 0.1496. To the best of our knowledge, this is the first study to use individually unique neuromuscular information during unobservable muscle isometric contractions for user authentication, with training and testing data acquired on different days.

The ability to expertly control different fingers contributes to hand dexterity during object manipulation in daily life activities. The macroscopic spatial patterns of muscle activations during finger movements using global surface electromyography (sEMG) have been widely researched. However, the spatial activation patterns of microscopic motor units (MUs) under different finger movements have not been well investigated. The present work aims to quantify MU spatial activation patterns during movement of distinct fingers (index, middle, ring and little finger). Specifically, we focused on extensor muscles during extension contractions. Motor unit action potentials (MUAPs) during movement of each finger were obtained through decomposition of high-density sEMG (HD-sEMG). First, we quantified the spatial activation patterns of MUs for each finger based on 2-dimension (2-D) root-mean-square (RMS) maps of MUAP grids after spike-triggered averaging. We found that these activation patterns under different finger movements are distinct along the distal-proximal direction, but with partial overlap. Second, to further evaluate MU separability, we classified the spatial activation pattern of each individual MU under distinct finger movement and associated each MU with its corresponding finger with Regularized Uncorrelated Multilinear Discriminant Analysis (RUMLDA). A high accuracy of MU-finger classification tested on 12 subjects with a mean of 88.98% was achieved. The quantification of MU spatial activation patterns could be beneficial to studies of neural mechanisms of the hand. To the best of our knowledge, this is the first work which manages to quantify MU behaviors under different finger movements.

With the soaring development of body sensor network (BSN)-based health informatics, information security in such medical devices has attracted increasing attention in recent years. Employing the biosignals acquired directly by the BSN as biometrics for personal identification is an effective approach. Noncancelability and cross-application invariance are two natural flaws of most traditional biometric modalities. Once the biometric template is exposed, it is compromised forever. Even worse, because the same biometrics may be employed as tokens for different accounts in multiple applications, the exposed template can be used to compromise other accounts. In this work, we propose a cancelable and cross-application discrepant biometric approach based on high-density surface electromyogram (HD-sEMG) for personal identification. We enrolled two accounts for each user. HD-sEMG signals from the right dorsal hand under isometric contractions of different finger muscles were employed as biometric tokens. Since isometric contraction, in contrast to dynamic contraction, requires no actual movement, the users' choice to login to different accounts is greatly protected against impostors. We realized a promising identification accuracy of 85.8% for 44 identities (22 subjects × 2 accounts) with training and testing data acquired 9 days apart. The high identification accuracy of different accounts for the same user demonstrates the promising cancelability and cross-application discrepancy of the proposed HD-sEMG-based biometrics. To the best of our knowledge, this is the first study to employ HD-sEMG in personal identification applications, with signal variation across days considered.

Photoplethysmography (PPG) has shown its great potential for noninvasive health monitoring, but its application in wearable devices is largely impeded due to its extreme vulnerability to motion artifacts. In this article, we proposed a new stochastic modeling based nonlinear Bayesian filtering framework for the recovery of corrupted PPG waveform under strenuous physical exercise in wearable health-monitoring devices. A deep recurrent neural network was first recruited for accurate cardiac-period segmentation of corrupted PPG signals. Then, a stochastic model was applied to extract waveform details from clean PPG pulses, and was further derived into a system-state space. Following this was an extended Kalman filter using the state-space structured by modeling. The covariance of measurement noise was estimated by motion-related information to adjust it into the real physical environment adaptively. Comparison results with state-of-the-art methods on a wearable-device-based 48-subject data set showed the outstanding performance of the proposed denoising framework, with period-segmentation sensitivity and precision higher than 99.1%, instantaneous heart rate (HR) error lower than 2 beats/min, average HR error down to 1.14 beats/min, and recovery accuracy of waveform details significantly improved (p <; 0.05). This framework is the first PPG denoising strategy that introduces waveform-modeling methods to ensure detail recovery, and a great example of algorithm fusion between stochastic signal processing and emerging deep learning methods for time-sequential biomedical signal processing.

Estimation of joint kinematics plays an important role in intuitive human-machine interactions. However, continuous and reliable estimation of small (e.g., the finger) joint angles is still a challenge. The objective of this study was to continuously estimate finger joint angles using populational motoneuron firing activities. Multi-channel surface electromyogram (sEMG) signals were obtained from the extensor digitorum communis muscles, while the subjects performed individual finger oscillatory extension movements at two different speeds. The individual finger movement was first classified based on the EMG signals. The discharge timings of individual motor units were extracted through high-density EMG decomposition, and were then pooled as a composite discharge train. The firing frequency of the populational motor unit firing events was used to represent the descending neural drive to the motor unit pool. A second-order polynomial regression was then performed to predict the measured metacarpophalangeal extension angle using the derived neural drive based on the neuronal firings. Our results showed that individual finger extension movement can be classified with >96% accuracy based on multi-channel EMG. The extension angles of individual fingers can be predicted continuously by the derived neural drive with R2values >0.8. The performance of the neural-drive-based approach was superior to the conventional EMG-amplitude-based approach, especially during fast movements. These findings indicated that the neural-drive-based interface was a promising approach to reliably predict individual finger kinematics.

The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94-100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed.

Extracting useful knowledge from social network datasets is a challenging problem. While large online social networks such as Facebook and LinkedIn are well known and gather millions of users, small social networks are today becoming increasingly common. Many corporations already use existing social networks to connect to their customers. Seeing the increasing usage of small social networks, such companies will likely start to create in-house online social networks where they will own the data shared by customers. The trustworthiness of these online social networks is essentially important for decision making of those companies. In this paper, our goal is to assess the trustworthiness of local social network data by referencing external social networks. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.

Extracting useful knowledge from social network datasets is a challenging problem. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extra

Assessing the trustworthiness of location data corresponding to individuals is essential in several applications, such as forensic science and epidemic control. To obtain accurate and trustworthy location data, analysts must often gather and correlate information from several independent sources, e.g., physical observation, witness testimony, surveillance footage, etc. However, such information may be fraudulent, its accuracy may be low, and its volume may be insufficient to ensure highly trustworthy data. On the other hand, recent advancements in mobile computing and positioning systems, e.g., GPS-enabled cell phones, highway sensors, etc., bring new and effective technological means to track the location of an individual. Nevertheless, collection and sharing of such data must be done in ways that do not violate an individual's right to personal privacy.

Data integrity is crucial for collaborative activities where information is shared among multiple organizations to effectively make cooperative and mission-critical decisions. Assuring data integrity is particularly challenging in the presence of frequent data modifications by collaborative parties,

Trustworthiness of location information about particular individuals is of particular interest in the areas of forensic science and epidemic control. In many cases, location information is not precise and may include fraudulent information. With the growth of mobile computing and positioning systems, e.g., GPS and cell phones, it has become possible to trace the location of moving objects. Such Systems provide us an opportunity to find out the true locations of individuals. In this paper, we present a model to compute trustworthiness of the location information of an individual based on different evidences from different sources. We also introduce a collusion attack that may bias the computation. Based on the analysis of the attack, we present the algorithm to detect and reduce the effect of collusion attacks. Our experimental results show the efficiency and effectiveness of our approach.

Releasing detailed data ( microdata ) about individuals poses a privacy threat, due to the presence of quasi-identifier (QID) attributes such as age or zip code. Several privacy paradigms have been proposed that preserve privacy by placing constraints on the value of released QIDs. However, in order to enforce these paradigms, data publishers need tools to assist them in selecting a suitable anonymization method and choosing the right system parameters. We developed TIAMAT , a tool for analysis of anonymization techniques which allows data publishers to assess the accuracy and overhead of existing anonymization techniques. The tool performs interactive, head-to-head comparison of anonymization techniques, as well as QID change-impact analysis. Other features include collection of attribute statistics, support for multiple information loss metrics and compatibility with commercial database engines.