Analysis of Paradoxes in Fingerprint Countermeasures

The widespread usage of new user tracking methods, i.e. web-based fingerprinting, is becoming a serious privacy concern as third parties try to track users across different websites. Meanwhile, it is usually difficult or impossible for users to opt-out fingerprinting if they want to fully benefit the services provided by the application or website. Several studies tried to address the privacy issue in browser fingerprinting, mostly by faking attribute values. However, such configuration spoofing may lead to inconsistencies that paradoxically make the user stand out even more. This study analyzes these paradoxes in browser configuration with the creation of a Markov model based on a test dataset. Given a target spoofed attribute, the implemented tool in this study outputs the other attributes that must be consequently altered, not to cause paradoxical configuration. Similarly, this tool can suggest a set of random attributes to be spoofed with suggested values, not creating a paradoxical configuration. The tool Implemented in this study can be used by browser extension developers and should help them spoof browser attributes more sophistically, thus preservingusers' privacy against cross-site web-based browser fingerprinting.Peer reviewe

SiMEA : a Framework for simulating neurons on Multi-Electrode Array

Proceeding volume: 38A Multi-Electrode Array (MEA) is a practical device for recording the extracellular activity of in-vitro biological culture. Such culture - for instance neurons - is prone to mistakes leading to irrelevant recordings or no recording at all. Additionally, with the expenses generated by in-vitro culture, minimizing risks is a must. This paper proposes a framework designed and implemented for simulating the spatial positioning of neuronal cultures on a MEA. The framework serves as a sandbox for researchers to simulate the model of their MEA experiments before its eventual in-vitro implementation. The framework enables simulating the density of the plated culture, the death of cells over time, choosing diverse reconstructed morphologies of cells, and simulating their spiking activity in interaction with Brian2 simulator.Peer reviewe

Criteria and Analysis for Human-Centered Browser Fingerprinting Countermeasures

Browser fingerprinting is a surveillance technique that uses browser and device attributes to track visitors across the web. Defeating fingerprinting requires blocking attribute information or spoofing attributes, which can result in loss of functionality. To address the challenge of escaping surveillance while obtaining functionality, we identify six design criteria for an ideal spoofing system. We present three fingerprint generation algorithms as well as a baseline algorithm that simply samples a dataset of fingerprints. For each algorithm, we identify trade-offs among the criteria: distinguishability from a non-spoofed fingerprint, uniqueness, size of the anonymity set, efficient generation, loss of web functionality, and whether or not the algorithm protects the confidentiality of the underlying dataset. We report on a series of experiments illustrating that the use of our partially-dependent algorithm for spoofing fingerprints will avoid detection by Machine Learning approaches to surveillance

Biophysical parameters control signal transfer in spiking network

IntroductionInformation transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic membrane properties, but how these relate to information transmission in a population of neurons is still poorly understood. A recent study investigated local learning rules and showed how a spiking neural network can learn to represent continuous signals. Our study builds on their model to explore how basic membrane properties and synaptic delays affect information transfer. MethodsThe system consisted of three input and output units and a hidden layer of 300 excitatory and 75 inhibitory leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units. After optimizing the connectivity to accurately replicate the input patterns in the output units, we transformed the model to more biologically accurate units and included synaptic delay and concurrent action potential generation in distinct neurons. We examined three different parameter regimes which comprised either identical physiological values for both excitatory and inhibitory units (Comrade), more biologically accurate values (Bacon), or the Comrade regime whose output units were optimized for low reconstruction error (HiFi). We evaluated information transmission and classification accuracy of the network with four distinct metrics: coherence, Granger causality, transfer entropy, and reconstruction error. ResultsBiophysical parameters showed a major impact on information transfer metrics. The classification was surprisingly robust, surviving very low firing and information rates, whereas information transmission overall and particularly low reconstruction error were more dependent on higher firing rates in LIF units. In AdEx units, the firing rates were lower and less information was transferred, but interestingly the highest information transmission rates were no longer overlapping with the highest firing rates. DiscussionOur findings can be reflected on the predictive coding theory of the cerebral cortex and may suggest information transfer qualities as a phenomenological quality of biological cells.Peer reviewe

Biophysical parameters control signal transfer in spiking network

IntroductionInformation transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic membrane properties, but how these relate to information transmission in a population of neurons is still poorly understood. A recent study investigated local learning rules and showed how a spiking neural network can learn to represent continuous signals. Our study builds on their model to explore how basic membrane properties and synaptic delays affect information transfer.MethodsThe system consisted of three input and output units and a hidden layer of 300 excitatory and 75 inhibitory leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units. After optimizing the connectivity to accurately replicate the input patterns in the output units, we transformed the model to more biologically accurate units and included synaptic delay and concurrent action potential generation in distinct neurons. We examined three different parameter regimes which comprised either identical physiological values for both excitatory and inhibitory units (Comrade), more biologically accurate values (Bacon), or the Comrade regime whose output units were optimized for low reconstruction error (HiFi). We evaluated information transmission and classification accuracy of the network with four distinct metrics: coherence, Granger causality, transfer entropy, and reconstruction error.ResultsBiophysical parameters showed a major impact on information transfer metrics. The classification was surprisingly robust, surviving very low firing and information rates, whereas information transmission overall and particularly low reconstruction error were more dependent on higher firing rates in LIF units. In AdEx units, the firing rates were lower and less information was transferred, but interestingly the highest information transmission rates were no longer overlapping with the highest firing rates.DiscussionOur findings can be reflected on the predictive coding theory of the cerebral cortex and may suggest information transfer qualities as a phenomenological quality of biological cells

Modeling Realistic Neuronal Activity in MEA Plates

This thesis work is part of a project from Academy of Finland aiming at integrating biological components in sensor networks. The current integration goal considers neuronal cultures for achieving data processing. Due to the high capacity of neuronal cultures in parallel computation, the main assumptions of this project are that such integration will enable data processing that is not achievable with electrical components, and will reduce energy consumption. Within the scope of this project, the objective of this thesis is to develop realistic computational models of neuronal cultures plated on Multi-Electrode Arrays (MEAs). MEAs are integrated circuits used for stimulating cell cultures and recording their electrophysiological activity. Such models are used in the project for feasibility simulations and preliminary developments of bio-integrated systems (BIS). The contribution of this thesis is twofold: modeling plausible neural cultures on MEA, and analysis of the connectivity of neural networks. The first part contributes in gaining an in-depth understanding of the behavior of the neural network in MEA plate. A simulation framework is designed, implemented and used to simulate the neuronal activity in a MEA plate containing 1000 neurons. Using the implemented framework, it is now possible to simulate a MEA plate with many customizable parameters, e.g. MEA size, neuron size, type and morphology. The second part contributes with two implementations of a method for functional analysis of neural networks. Two GPU-accelerated algorithms of the Cox method were implemented with the CUDA platform. The Cox method is a proven robust method for the analysis of functional connectivity in networks. This method, formerly demanding a long time as well as consequent CPU power, can now run hundreds of times faster on CUDA-supported GPUs in personal computers

Modeling Realistic Neuronal Activity in MEA Plates

This thesis work is part of a project from Academy of Finland aiming at integrating biological components in sensor networks. The current integration goal considers neuronal cultures for achieving data processing. Due to the high capacity of neuronal cultures in parallel computation, the main assumptions of this project are that such integration will enable data processing that is not achievable with electrical components, and will reduce energy consumption. Within the scope of this project, the objective of this thesis is to develop realistic computational models of neuronal cultures plated on Multi-Electrode Arrays (MEAs). MEAs are integrated circuits used for stimulating cell cultures and recording their electrophysiological activity. Such models are used in the project for feasibility simulations and preliminary developments of bio-integrated systems (BIS). The contribution of this thesis is twofold: modeling plausible neural cultures on MEA, and analysis of the connectivity of neural networks. The first part contributes in gaining an in-depth understanding of the behavior of the neural network in MEA plate. A simulation framework is designed, implemented and used to simulate the neuronal activity in a MEA plate containing 1000 neurons. Using the implemented framework, it is now possible to simulate a MEA plate with many customizable parameters, e.g. MEA size, neuron size, type and morphology. The second part contributes with two implementations of a method for functional analysis of neural networks. Two GPU-accelerated algorithms of the Cox method were implemented with the CUDA platform. The Cox method is a proven robust method for the analysis of functional connectivity in networks. This method, formerly demanding a long time as well as consequent CPU power, can now run hundreds of times faster on CUDA-supported GPUs in personal computers

Controlling Complexity of Cerebral Cortex Simulations-I : CxSystem, a Flexible Cortical Simulation Framework

Simulation of the cerebral cortex requires a combination of extensive domain-specific knowledge and efficient software. However, when the complexity of the biological system is combined with that of the software, the likelihood of coding errors increases, which slows model adjustments. Moreover, few life scientists are familiar with software engineering and would benefit from simplicity in form of a high-level abstraction of the biological model. Our primary aim was to build a scalable cortical simulation framework for personal computers. We isolated an adjustable part of the domain-specific knowledge from the software. Next, we designed a framework that reads the model parameters from comma-separated value files and creates the necessary code for Brian2 model simulation. This separation allows rapid exploration of complex cortical circuits while decreasing the likelihood of coding errors and automatically using efficient hardware devices. Next, we tested the system on a simplified version of the neocortical microcircuit proposed by Markram and colleagues (2015). Our results indicate that the framework can efficiently perform simulations using Python, C++, and GPU devices. The most efficient device varied with computer hardware and the duration and scale of the simulated system. The speed of Brian2 was retained despite an overlying layer of software. However, the Python and C++ devices inherited the single core limitation of Brian2. The CxSystem framework supports exploration of complex models on personal computers and thus has the potential to facilitate research on cortical networks and systems.Peer reviewe

Controlling Complexity of Cerebral Cortex Simulations-II : Streamlined Microcircuits

Recently, Markram et al. (2015) presented a model of the rat somatosensory microcircuit (Markram model). Their model is high in anatomical and physiological detail, and its simulation requires supercomputers. The lack of neuroinformatics and computing power is an obstacle for using a similar approach to build models of other cortical areas or larger cortical systems. Simplified neuron models offer an attractive alternative to high-fidelity Hodgkin-Huxley-type neuron models, but their validity in modeling cortical circuits is unclear. We simplified the Markram model to a network of exponential integrate-and-fire (EIF) neurons that runs on a single CPU core in reasonable time. We analyzed the electrophysiology and the morphology of the Markram model neurons with eFel and NeuroM tools, provided by the Blue Brain Project. We then constructed neurons with few compartments and averaged parameters from the reference model. We used the CxSystem simulation framework to explore the role of short-term plasticity and GABAB and NMDA synaptic conductances in replicating oscillatory phenomena in the Markram model. We show that having a slow inhibitory synaptic conductance (GABAB) allows replication of oscillatory behavior in the high-calcium state. Furthermore, we show that qualitatively similar dynamics are seen even with a reduced number of cell types (from 55 to 17 types). This reduction halved the computation time. Our results suggest that qualitative dynamics of cortical microcircuits can be studied using limited neuroinformatics and computing resources supporting parameter exploration and simulation of cortical systems. The simplification procedure can easily be adapted to studying other microcircuits for which sparse electrophysiological and morphological data are available.Peer reviewe

Analysis of Paradoxes in Fingerprint Countermeasures

The widespread usage of new user tracking methods, i.e. web-based fingerprinting, is becoming a serious privacy concern as third parties try to track users across different websites. Meanwhile, it is usually difficult or impossible for users to opt-out fingerprinting if they want to fully benefit the services provided by the application or website. Several studies tried to address the privacy issue in browser fingerprinting, mostly by faking attribute values. However, such configuration spoofing may lead to inconsistencies that paradoxically make the user stand out even more. This study analyzes these paradoxes in browser configuration with the creation of a Markov model based on a test dataset. Given a target spoofed attribute, the implemented tool in this study outputs the other attributes that must be consequently altered, not to cause paradoxical configuration. Similarly, this tool can suggest a set of random attributes to be spoofed with suggested values, not creating a paradoxical configuration. The tool Implemented in this study can be used by browser extension developers and should help them spoof browser attributes more sophistically, thus preserving users' privacy against cross-site web-based browser fingerprinting