Visible to the public Biblio

Found 3549 results

Filters: Keyword is composability  [Clear All Filters]
2021-01-20
Wang, H., Yang, J., Wang, X., Li, F., Liu, W., Liang, H..  2020.  Feature Fingerprint Extraction and Abnormity Diagnosis Method of the Vibration on the GIS. 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE). :1—4.

Mechanical faults of Gas Insulated Switchgear (GIS) often occurred, which may cause serious losses. Detecting vibration signal was effective for condition monitoring and fault diagnosis of GIS. The vibration characteristic of GIS in service was detected and researched based on a developed testing system in this paper, and feature fingerprint extraction method was proposed to evaluate vibration characteristics and diagnose mechanical defects. Through analyzing the spectrum of the vibration signal, we could see that vibration frequency of operating GIS was about 100Hz under normal condition. By means of the wavelet transformation, the vibration fingerprint was extracted for the diagnosis of mechanical vibration. The mechanical vibration characteristic of GIS including circuit breaker and arrester in service was detected, we could see that the frequency distribution of abnormal vibration signal was wider, it contained a lot of high harmonic components besides the 100Hz component, and the vibration acoustic fingerprint was totally different from the normal ones, that is, by comparing the frequency spectra and vibration fingerprint, the mechanical faults of GIS could be found effectively.

Li, M., Chang, H., Xiang, Y., An, D..  2020.  A Novel Anti-Collusion Audio Fingerprinting Scheme Based on Fourier Coefficients Reversing. IEEE Signal Processing Letters. 27:1794—1798.

Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this letter, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.

Shi, F., Chen, Z., Cheng, X..  2020.  Behavior Modeling and Individual Recognition of Sonar Transmitter for Secure Communication in UASNs. IEEE Access. 8:2447—2454.

It is necessary to improve the safety of the underwater acoustic sensor networks (UASNs) since it is mostly used in the military industry. Specific emitter identification is the process of identifying different transmitters based on the radio frequency fingerprint extracted from the received signal. The sonar transmitter is a typical low-frequency radiation source and is an important part of the UASNs. Class D power amplifier, a typical nonlinear amplifier, is usually used in sonar transmitters. The inherent nonlinearity of power amplifiers provides fingerprint features that can be distinguished without transmitters for specific emitter recognition. First, the nonlinearity of the sonar transmitter is studied in-depth, and the nonlinearity of the power amplifier is modeled and its nonlinearity characteristics are analyzed. After obtaining the nonlinear model of an amplifier, a similar amplifier in practical application is obtained by changing its model parameters as the research object. The output signals are collected by giving the same input of different models, and, then, the output signals are extracted and classified. In this paper, the memory polynomial model is used to model the amplifier. The power spectrum features of the output signals are extracted as fingerprint features. Then, the dimensionality of the high-dimensional features is reduced. Finally, the classifier is used to recognize the amplifier. The experimental results show that the individual sonar transmitter can be well identified by using the nonlinear characteristics of the signal. By this way, this method can enhance the communication safety of the UASNs.

Lei, M., Jin, M., Huang, T., Guo, Z., Wang, Q., Wu, Z., Chen, Z., Chen, X., Zhang, J..  2020.  Ultra-wideband Fingerprinting Positioning Based on Convolutional Neural Network. 2020 International Conference on Computer, Information and Telecommunication Systems (CITS). :1—5.

The Global Positioning System (GPS) can determine the position of any person or object on earth based on satellite signals. But when inside the building, the GPS cannot receive signals, the indoor positioning system will determine the precise position. How to achieve more precise positioning is the difficulty of an indoor positioning system now. In this paper, we proposed an ultra-wideband fingerprinting positioning method based on a convolutional neural network (CNN), and we collect the dataset in a room to test the model, then compare our method with the existing method. In the experiment, our method can reach an accuracy of 98.36%. Compared with other fingerprint positioning methods our method has a great improvement in robustness. That results show that our method has good practicality while achieves higher accuracy.

Aman, W., Haider, Z., Shah, S. W. H., Rahman, M. M. Ur, Dobre, O. A..  2020.  On the Effective Capacity of an Underwater Acoustic Channel under Impersonation Attack. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—7.

This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob); Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GTJ method.

Mehmood, Z., Qazi, K. Ashfaq, Tahir, M., Yousaf, R. Muhammad, Sardaraz, M..  2020.  Potential Barriers to Music Fingerprinting Algorithms in the Presence of Background Noise. 2020 6th Conference on Data Science and Machine Learning Applications (CDMA). :25—30.

An acoustic fingerprint is a condensed and powerful digital signature of an audio signal which is used for audio sample identification. A fingerprint is the pattern of a voice or audio sample. A large number of algorithms have been developed for generating such acoustic fingerprints. These algorithms facilitate systems that perform song searching, song identification, and song duplication detection. In this study, a comprehensive and powerful survey of already developed algorithms is conducted. Four major music fingerprinting algorithms are evaluated for identifying and analyzing the potential hurdles that can affect their results. Since the background and environmental noise reduces the efficiency of music fingerprinting algorithms, behavioral analysis of fingerprinting algorithms is performed using audio samples of different languages and under different environmental conditions. The results of music fingerprint classification are more successful when deep learning techniques for classification are used. The testing of the acoustic feature modeling and music fingerprinting algorithms is performed using the standard dataset of iKala, MusicBrainz and MIR-1K.

Jiang, M., Lundgren, J., Pasha, S., Carratù, M., Liguori, C., Thungström, G..  2020.  Indoor Silent Object Localization using Ambient Acoustic Noise Fingerprinting. 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). :1—6.

Indoor localization has been a popular research subject in recent years. Usually, object localization using sound involves devices on the objects, acquiring data from stationary sound sources, or by localizing the objects with external sensors when the object generates sounds. Indoor localization systems using microphones have traditionally also used systems with several microphones, setting the limitations on cost efficiency and required space for the systems. In this paper, the goal is to investigate whether it is possible for a stationary system to localize a silent object in a room, with only one microphone and ambient noise as information carrier. A subtraction method has been combined with a fingerprint technique, to define and distinguish the noise absorption characteristic of the silent object in the frequency domain for different object positions. The absorption characteristics of several positions of the object is taken as comparison references, serving as fingerprints of known positions for an object. With the experiment result, the tentative idea has been verified as feasible, and noise signal based lateral localization of silent objects can be achieved.

Zarazaga, P. P., Bäckström, T., Sigg, S..  2020.  Acoustic Fingerprints for Access Management in Ad-Hoc Sensor Networks. IEEE Access. 8:166083—166094.

Voice user interfaces can offer intuitive interaction with our devices, but the usability and audio quality could be further improved if multiple devices could collaborate to provide a distributed voice user interface. To ensure that users' voices are not shared with unauthorized devices, it is however necessary to design an access management system that adapts to the users' needs. Prior work has demonstrated that a combination of audio fingerprinting and fuzzy cryptography yields a robust pairing of devices without sharing the information that they record. However, the robustness of these systems is partially based on the extensive duration of the recordings that are required to obtain the fingerprint. This paper analyzes methods for robust generation of acoustic fingerprints in short periods of time to enable the responsive pairing of devices according to changes in the acoustic scenery and can be integrated into other typical speech processing tools.

Sato, Y., Yanagitani, T..  2020.  Giga-hertz piezoelectric epitaxial PZT transducer for the application of fingerprint imaging. 2020 IEEE International Ultrasonics Symposium (IUS). :1—3.

The fingerprint sensor based on pMUTs was reported [1]. Spatial resolution of the image depends on the size of the acoustic source when a plane wave is used. If the size of the acoustic source is smaller, piezoelectric films with high dielectric constant are required. In this study, in order to obtain small acoustic source, we proposed Pb(Zrx Th-x)O3 (PZT) epitaxial transducers with high dielectric constant. PbTiO3 (PTO) epitaxial films were grown on conductive La-SrTiO3 (STO) substrate by RF magnetron sputtering. Longitudinal wave conversion loss of PTO transducers was measured by a network analyzer. The thermoplastic elastomer was used instead of real fingerprint. We confirmed that conversion loss of piezoelectric film/substrate structure was increased by contacting the elastomer due the change of reflection coefficient of the substrate bottom/elastomer interface. Minimum conversion loss images were obtained by mechanically scanning the soft probe on the transducer surface. We achieved the detection of the fingerprint phantom based on the elastomer in the GHz.

2021-01-11
Johnson, N., Near, J. P., Hellerstein, J. M., Song, D..  2020.  Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms. 2020 IEEE European Symposium on Security and Privacy (EuroS P). :535–551.
Differential privacy is fast becoming the gold standard in enabling statistical analysis of data while protecting the privacy of individuals. However, practical use of differential privacy still lags behind research progress because research prototypes cannot satisfy the scalability requirements of production deployments. To address this challenge, we present Chorus, a framework for building scalable differential privacy mechanisms which is based on cooperation between the mechanism itself and a high-performance production database management system (DBMS). We demonstrate the use of Chorus to build the first highly scalable implementations of complex mechanisms like Weighted PINQ, MWEM, and the matrix mechanism. We report on our experience deploying Chorus at Uber, and evaluate its scalability on real-world queries.
Jiang, P., Liao, S..  2020.  Differential Privacy Online Learning Based on the Composition Theorem. 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC). :200–203.
Privacy protection is becoming more and more important in the era of big data. Differential privacy is a rigorous and provable privacy protection method that can protect privacy for a single piece of data. But existing differential privacy online learning methods have great limitations in the scope of application and accuracy. Aiming at this problem, we propose a more general and accurate algorithm, named DPOL-CT, for differential privacy online learning. We first distinguish the difference in differential privacy protection between offline learning and online learning. Then we prove that the DPOL-CT algorithm achieves (∊, δ)-differential privacy for online learning under the Gaussian, the Laplace and the Staircase mechanisms and enjoys a sublinear expected regret bound. We further discuss the trade-off between the differential privacy level and the regret bound. Theoretical analysis and experimental results show that the DPOL-CT algorithm has good performance guarantees.
Lobo-Vesga, E., Russo, A., Gaboardi, M..  2020.  A Programming Framework for Differential Privacy with Accuracy Concentration Bounds. 2020 IEEE Symposium on Security and Privacy (SP). :411–428.
Differential privacy offers a formal framework for reasoning about privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analyses results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages aimed at helping a data analyst in programming differentially private analyses. However, most of the programming languages for differential privacy proposed so far provide support for reasoning about privacy but not for reasoning about the accuracy of data analyses. To overcome this limitation, in this work we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy and their trade-offs. The distinguishing feature of DPella is a novel component which statically tracks the accuracy of different data analyses. In order to make tighter accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We evaluate our approach by implementing several classical queries from the literature and showing how data analysts can figure out the best manner to calibrate privacy to meet the accuracy requirements.
Li, Y., Chang, T.-H., Chi, C.-Y..  2020.  Secure Federated Averaging Algorithm with Differential Privacy. 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). :1–6.
Federated learning (FL), as a recent advance of distributed machine learning, is capable of learning a model over the network without directly accessing the client's raw data. Nevertheless, the clients' sensitive information can still be exposed to adversaries via differential attacks on messages exchanged between the parameter server and clients. In this paper, we consider the widely used federating averaging (FedAvg) algorithm and propose to enhance the data privacy by the differential privacy (DP) technique, which obfuscates the exchanged messages by properly adding Gaussian noise. We analytically show that the proposed secure FedAvg algorithm maintains an O(l/T) convergence rate, where T is the total number of stochastic gradient descent (SGD) updates for local model parameters. Moreover, we demonstrate how various algorithm parameters can impact on the algorithm communication efficiency. Experiment results are presented to justify the obtained analytical results on the performance of the proposed algorithm in terms of testing accuracy.
Farokhi, F..  2020.  Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon. 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS). :1–8.
We define discounted differential privacy, as an alternative to (conventional) differential privacy, to investigate privacy of evolving datasets, containing time series over an unbounded horizon. We use privacy loss as a measure of the amount of information leaked by the reports at a certain fixed time. We observe that privacy losses are weighted equally across time in the definition of differential privacy, and therefore the magnitude of privacy-preserving additive noise must grow without bound to ensure differential privacy over an infinite horizon. Motivated by the discounted utility theory within the economics literature, we use exponential and hyperbolic discounting of privacy losses across time to relax the definition of differential privacy under continual observations. This implies that privacy losses in distant past are less important than the current ones to an individual. We use discounted differential privacy to investigate privacy of evolving datasets using additive Laplace noise and show that the magnitude of the additive noise can remain bounded under discounted differential privacy. We illustrate the quality of privacy-preserving mechanisms satisfying discounted differential privacy on smart-meter measurement time-series of real households, made publicly available by Ausgrid (an Australian electricity distribution company).
Wu, N., Farokhi, F., Smith, D., Kaafar, M. A..  2020.  The Value of Collaboration in Convex Machine Learning with Differential Privacy. 2020 IEEE Symposium on Security and Privacy (SP). :304–317.
In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the quality of the trained model, using the fitness cost, as a function of privacy budget and size of the distributed datasets to capture the trade-off between privacy and utility in machine learning. This way, we can predict the outcome of collaboration among privacy-aware data owners prior to executing potentially computationally-expensive machine learning algorithms. Particularly, we show that the difference between the fitness of the trained machine learning model using differentially-private gradient queries and the fitness of the trained machine model in the absence of any privacy concerns is inversely proportional to the size of the training datasets squared and the privacy budget squared. We successfully validate the performance prediction with the actual performance of the proposed privacy-aware learning algorithms, applied to: financial datasets for determining interest rates of loans using regression; and detecting credit card frauds using support vector machines.
Xin, B., Yang, W., Geng, Y., Chen, S., Wang, S., Huang, L..  2020.  Private FL-GAN: Differential Privacy Synthetic Data Generation Based on Federated Learning. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2927–2931.
Generative Adversarial Network (GAN) has already made a big splash in the field of generating realistic "fake" data. However, when data is distributed and data-holders are reluctant to share data for privacy reasons, GAN's training is difficult. To address this issue, we propose private FL-GAN, a differential privacy generative adversarial network model based on federated learning. By strategically combining the Lipschitz limit with the differential privacy sensitivity, the model can generate high-quality synthetic data without sacrificing the privacy of the training data. We theoretically prove that private FL-GAN can provide strict privacy guarantee with differential privacy, and experimentally demonstrate our model can generate satisfactory data.
Lyu, L..  2020.  Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
The appearance of distributed learning allows multiple participants to collaboratively train a global model, where instead of directly releasing their private training data with the server, participants iteratively share their local model updates (parameters) with the server. However, recent attacks demonstrate that sharing local model updates is not sufficient to provide reasonable privacy guarantees, as local model updates may result in significant privacy leakage about local training data of participants. To address this issue, in this paper, we present an alternative approach that combines distributed differential privacy (DDP) with a three-layer encryption protocol to achieve a better privacy-utility tradeoff than the existing DP-based approaches. An unbiased encoding algorithm is proposed to cope with floating-point values, while largely reducing mean squared error due to rounding. Our approach dispenses with the need for any trusted server, and enables each party to add less noise to achieve the same privacy and similar utility guarantees as that of the centralized differential privacy. Preliminary analysis and performance evaluation confirm the effectiveness of our approach, which achieves significantly higher accuracy than that of local differential privacy approach, and comparable accuracy to the centralized differential privacy approach.
Wang, J., Wang, A..  2020.  An Improved Collaborative Filtering Recommendation Algorithm Based on Differential Privacy. 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). :310–315.
In this paper, differential privacy protection method is applied to matrix factorization method that used to solve the recommendation problem. For centralized recommendation scenarios, a collaborative filtering recommendation model based on matrix factorization is established, and a matrix factorization mechanism satisfying ε-differential privacy is proposed. Firstly, the potential characteristic matrix of users and projects is constructed. Secondly, noise is added to the matrix by the method of target disturbance, which satisfies the differential privacy constraint, then the noise matrix factorization model is obtained. The parameters of the model are obtained by the stochastic gradient descent algorithm. Finally, the differential privacy matrix factorization model is used for score prediction. The effectiveness of the algorithm is evaluated on the public datasets including Movielens and Netflix. The experimental results show that compared with the existing typical recommendation methods, the new matrix factorization method with privacy protection can recommend within a certain range of recommendation accuracy loss while protecting the users' privacy information.
2020-12-28
Liu, H., Di, W..  2020.  Application of Differential Privacy in Location Trajectory Big Data. 2020 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS). :569—573.

With the development of mobile internet technology, GPS technology and social software have been widely used in people's lives. The problem of big data privacy protection related to location trajectory is becoming more and more serious. The traditional location trajectory privacy protection method requires certain background knowledge and it is difficult to adapt to massive mass. Privacy protection of data. differential privacy protection technology protects privacy by attacking data by randomly perturbing raw data. The method used in this paper is to first sample the position trajectory, form the irregular polygons of the high-frequency access points in the sampling points and position data, calculate the center of gravity of the polygon, and then use the differential privacy protection algorithm to add noise to the center of gravity of the polygon to form a new one. The center of gravity, and the new center of gravity are connected to form a new trajectory. The purpose of protecting the position trajectory is well achieved. It is proved that the differential privacy protection algorithm can effectively protect the position trajectory by adding noise.

Wang, A., Yuan, Z., He, B..  2020.  Design and Realization of Smart Home Security System Based on AWS. 2020 International Conference on Information Science, Parallel and Distributed Systems (ISPDS). :291—295.
With the popularization and application of Internet of Things technology, the degree of intelligence of the home system is getting higher and higher. As an important part of the smart home, the security system plays an important role in protecting against accidents such as flammable gas leakage, fire, and burglary that may occur in the home environment. This design focuses on sensor signal acquisition and processing, wireless access, and cloud applications, and integrates Cypress’s new generation of PSoC 6 MCU, CYW4343W Wi-Fi and Bluetooth dual-module chips, and Amazon’s AWS cloud into smart home security System designing. First, through the designed air conditioning and refrigeration module, fire warning processing module, lighting control module, ventilation fan control module, combustible gas and smoke detection and warning module, important parameter information in the home environment is obtained. Then, the hardware system is connected to the AWS cloud platform through Wi-Fi; finally, a WEB interface is built in the AWS cloud to realize remote monitoring of the smart home environment. This design has a good reference for the design of future smart home security systems.
Zhang, Y., Weng, J., Ling, Z., Pearson, B., Fu, X..  2020.  BLESS: A BLE Application Security Scanning Framework. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :636—645.
Bluetooth Low Energy (BLE) is a widely adopted wireless communication technology in the Internet of Things (IoT). BLE offers secure communication through a set of pairing strategies. However, these pairing strategies are obsolete in the context of IoT. The security of BLE based devices relies on physical security, but a BLE enabled IoT device may be deployed in a public environment without physical security. Attackers who can physically access a BLE-based device will be able to pair with it and may control it thereafter. Therefore, manufacturers may implement extra authentication mechanisms at the application layer to address this issue. In this paper, we design and implement a BLE Security Scan (BLESS) framework to identify those BLE apps that do not implement encryption or authentication at the application layer. Taint analysis is used to track if BLE apps use nonces and cryptographic keys, which are critical to cryptographic protocols. We scan 1073 BLE apps and find that 93% of them are not secure. To mitigate this problem, we propose and implement an application-level defense with a low-cost \$0.55 crypto co-processor using public key cryptography.
Sanjay, K. N., Shaila, K., Venugopal, K. R..  2020.  LA-ANA based Architecture for Bluetooth Environment. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :222—226.
Wireless Personal Area Network is widely used in day to day life. It might be a static or dynamic environment. As the density of the nodes increases it becomes difficult to handle the situation. The need of multiple sensor node technology in a desired environment without congestion is required. The use of autonomic network provides one such solution. The autonomicity combines the local automate and address agnostic features that controls the congestion resulting in improved throughput, fault tolerance and also with unicast and multicast packets delivery. The algorithm LA based ANA in a Bluetooth based dynamic environment provide 20% increase in throughput compared with LACAS based Wireless Sensor Network. The LA based ANA leads with 10% lesser fault tolerance levels and extended unicast and multi-cast packet delivery.
Helluy-Lafont, É, Boé, A., Grimaud, G., Hauspie, M..  2020.  Bluetooth devices fingerprinting using low cost SDR. 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC). :289—294.
Physical fingerprinting is a trending domain in wireless security. Those methods aim at identifying transmitters based on the subtle variations existing in their handling of a communication protocol. They can provide an additional authentication layer, hard to emulate, to improve the security of systems. Software Defined Radios (SDR) are a tool of choice for the fingerprinting, as they virtually enable the analysis of any wireless communication scheme. However, they require expensive computations, and are still complex to handle by newcomers. In this paper, we use low cost SDR to propose a physical-layer fingerprinting approach, that allows recognition of the model of a device performing a Bluetooth scan, with more than 99.8% accuracy in a set of ten devices.
Cominelli, M., Gringoli, F., Patras, P., Lind, M., Noubir, G..  2020.  Even Black Cats Cannot Stay Hidden in the Dark: Full-band De-anonymization of Bluetooth Classic Devices. 2020 IEEE Symposium on Security and Privacy (SP). :534—548.

Bluetooth Classic (BT) remains the de facto connectivity technology in car stereo systems, wireless headsets, laptops, and a plethora of wearables, especially for applications that require high data rates, such as audio streaming, voice calling, tethering, etc. Unlike in Bluetooth Low Energy (BLE), where address randomization is a feature available to manufactures, BT addresses are not randomized because they are largely believed to be immune to tracking attacks. We analyze the design of BT and devise a robust de-anonymization technique that hinges on the apparently benign information leaking from frame encoding, to infer a piconet's clock, hopping sequence, and ultimately the Upper Address Part (UAP) of the master device's physical address, which are never exchanged in clear. Used together with the Lower Address Part (LAP), which is present in all frames transmitted, this enables tracking of the piconet master, thereby debunking the privacy guarantees of BT. We validate this attack by developing the first Software-defined Radio (SDR) based sniffer that allows full BT spectrum analysis (79 MHz) and implements the proposed de-anonymization technique. We study the feasibility of privacy attacks with multiple testbeds, considering different numbers of devices, traffic regimes, and communication ranges. We demonstrate that it is possible to track BT devices up to 85 meters from the sniffer, and achieve more than 80% device identification accuracy within less than 1 second of sniffing and 100% detection within less than 4 seconds. Lastly, we study the identified privacy attack in the wild, capturing BT traffic at a road junction over 5 days, demonstrating that our system can re-identify hundreds of users and infer their commuting patterns.

Zhang, C., Shahriar, H., Riad, A. B. M. K..  2020.  Security and Privacy Analysis of Wearable Health Device. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :1767—1772.
Mobile wearable health devices have expanded prevalent usage and become very popular because of the valuable health monitor system. These devices provide general health tips and monitoring human health parameters as well as generally assisting the user to take better health of themselves. However, these devices are associated with security and privacy risk among the consumers because these devices deal with sensitive data information such as users sleeping arrangements, dieting formula such as eating constraint, pulse rate and so on. In this paper, we analyze the significant security and privacy features of three very popular health tracker devices: Fitbit, Jawbone and Google Glass. We very carefully analyze the devices' strength and how the devices communicate and its Bluetooth pairing process with mobile devices. We explore the possible malicious attack through Bluetooth networking by hacker. The outcomes of this analysis show how these devices allow third parties to gain sensitive information from the device exact location that causes the potential privacy breach for users. We analyze the reasons of user data security and privacy are gained by unauthorized people on wearable devices and the possible challenge to secure user data as well as the comparison of three wearable devices (Fitbit, Jawbone and Google Glass) security vulnerability and attack type.