Biblio
We explore a new security model for secure computation on large datasets. We assume that two servers have been employed to compute on private data that was collected from many users, and, in order to improve the efficiency of their computation, we establish a new tradeoff with privacy. Specifically, instead of claiming that the servers learn nothing about the input values, we claim that what they do learn from the computation preserves the differential privacy of the input. Leveraging this relaxation of the security model allows us to build a protocol that leaks some information in the form of access patterns to memory, while also providing a formal bound on what is learned from the leakage. We then demonstrate that this leakage is useful in a broad class of computations. We show that computations such as histograms, PageRank and matrix factorization, which can be performed in common graph-parallel frameworks such as MapReduce or Pregel, benefit from our relaxation. We implement a protocol for securely executing graph-parallel computations, and evaluate the performance on the three examples just mentioned above. We demonstrate marked improvement over prior implementations for these computations.
Protocols for securely testing the equality of two encrypted integers are common building blocks for a number of proposals in the literature that aim for privacy preservation. Being used repeatedly in many cryptographic protocols, designing efficient equality testing protocols is important in terms of computation and communication overhead. In this work, we consider a scenario with two parties where party A has two integers encrypted using an additively homomorphic scheme and party B has the decryption key. Party A would like to obtain an encrypted bit that shows whether the integers are equal or not but nothing more. We propose three secure equality testing protocols, which are more efficient in terms of communication, computation or both compared to the existing work. To support our claims, we present experimental results, which show that our protocols achieve up to 99% computation-wise improvement compared to the state-of-the-art protocols in a fair experimental set-up.
Infrastructure-as-a-Service clouds provide out-of-band remote management for users to access their virtual machines (VMs). Out-of-band remote management is a method for indirectly accessing VMs via their virtual devices. While virtual devices running in the virtualized system are managed by cloud operators, not all cloud operators are always trusted in clouds. To prevent information leakage from virtual devices and tampering with their I/O data, several systems have been proposed by trusting the hypervisor in the virtualized system. However, they have various issues on security and management. This paper proposes VSBypass, which enables secure out-of-band remote management outside the virtualized system using a technique called transparent passthrough. VSBypass runs the entire virtualized system in an outer VM using nested virtualization. Then it intercepts I/O requests of out-of-band remote management and processes those requests in shadow devices, which run outside the virtualized system. We have implemented VSBypass in Xen for the virtual serial console and GUI remote access. We confirmed that information leakage was prevented and that the performance was comparable to that in traditional out-of-band remote management.
Secure network coding realizes the secrecy of the message when the message is transmitted via noiseless network and a part of edges or a part of intermediate nodes are eavesdropped. In this framework, if the channels of the network has noise, we apply the error correction to noisy channel before applying the secure network coding. In contrast, secure physical layer network coding is a method to securely transmit a message by a combination of coding operation on nodes when the network is given as a set of noisy channels. In this paper, we give several examples of network, in which, secure physical layer network coding realizes a performance that cannot be realized by secure network coding.
Quick Response (QR) codes are rapidly becoming pervasive in our daily life because of its fast readability and the popularity of smartphones with a built-in camera. However, recent researches raise security concerns because QR codes can be easily sniffed and decoded which can lead to private information leakage or financial loss. To address the issue, we present mQRCode which exploit patterns with specific spatial frequency to camouflage QR codes. When the targeted receiver put a camera at the designated position (e.g., 30cm and 0° above the camouflaged QR code), the original QR code is revealed due to the Moiré phenomenon. Malicious adversaries will only see camouflaged QR code at any other position. Our experiments show that the decoding rate of mQR codes is 95% or above within 0.83 seconds. When the camera is 10cm or 15° away from the designated location, the decoding rate drops to 0 so it's secure from attackers.
We introduce a new defense mechanism for stochastic control systems with control objectives, to enhance their resilience before the detection of any attacks. To this end, we cautiously design the outputs of the sensors that monitor the state of the system since the attackers need the sensor outputs for their malicious objectives in stochastic control scenarios. Different from the defense mechanisms that seek to detect infiltration or to improve detectability of the attacks, the proposed approach seeks to minimize the damage of possible attacks before they actually have even been detected. We, specifically, consider a controlled Gauss-Markov process, where the controller could have been infiltrated into at any time within the system's operation. Within the framework of game-theoretic hierarchical equilibrium, we provide a semi-definite programming based algorithm to compute the optimal linear secure sensor outputs that enhance the resiliency of control systems prior to attack detection.
The barcode is an important link between real life and the virtual world nowadays. One of the most common barcodes is QR code, which its appearance, black and white modules, is not visually pleasing. The QR code is applied to product packaging and campaign promotion in the market. There are more and more stores using QR code for transaction payment. If the QR code is altered or illegally duplicated, it will endanger the information security of users. Therefore, the study uses infrared watermarking to embed the infrared QR code information into the explicit QR code to strengthen the anti-counterfeiting features. The explicit graphic QR code is produced by data hiding with error diffusion in this study. With the optical characteristics of K, one of the four printed ink colors CMYK (Cyan, Magenta, Yellow, Black), only K can be rendered in infrared. Hence, we use the infrared watermarking to embed the implicit QR code information into the explicit graphic QR code. General QR code reader may be used to interpret explicit graphic QR code information. As for implicit QR code, it needs the infrared detector to extract its implicit QR code information. If the QR code is illegally copied, it will not show the hidden second QR code under infrared detection. In this study, infrared watermark hidden in the graphic QR code can enhance not only the aesthetics of QR code, but also the anti-counterfeiting feature. It can also be applied to printing related fields, such as security documents, banknotes, etc. in the future.
The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images. This underscores the urgent need for practical defense techniques that can be readily deployed to combat attacks in real-time. Observing that many attack strategies aim to perturb image pixels in ways that are visually imperceptible, we place JPEG compression at the core of our proposed SHIELD defense framework, utilizing its capability to effectively "compress away" such pixel manipulation. To immunize a DNN model from artifacts introduced by compression, SHIELD "vaccinates" the model by retraining it with compressed images, where different compression levels are applied to generate multiple vaccinated models that are ultimately used together in an ensemble defense. On top of that, SHIELD adds an additional layer of protection by employing randomization at test time that compresses different regions of an image using random compression levels, making it harder for an adversary to estimate the transformation performed. This novel combination of vaccination, ensembling, and randomization makes SHIELD a fortified multi-pronged defense. We conducted extensive, large-scale experiments using the ImageNet dataset, and show that our approaches eliminate up to 98% of gray-box attacks delivered by strong adversarial techniques such as Carlini-Wagner's L2 attack and DeepFool. Our approaches are fast and work without requiring knowledge about the model.
Recently, IoT, 5G mobile, big data, and artificial intelligence are increasingly used in the real world. These technologies are based on convergenced in Cyber Physical System(Cps). Cps technology requires core technologies to ensure reliability, real-time, safety, autonomy, and security. CPS is the system that can connect between cyberspace and physical space. Cyberspace attacks are confused in the real world and have a lot of damage. The personal information that dealing in CPS has high confidentiality, so the policies and technique will needed to protect the attack in advance. If there is an attack on the CPS, not only personal information but also national confidential data can be leaked. In order to prevent this, the risk is measured using the Factor Analysis of Information Risk (FAIR) Model, which can measure risk by element for situational awareness in CPS environment. To reduce risk by preventing attacks in CPS, this paper measures risk after using the concept of Crime Prevention Through Environmental Design(CPTED).
We introduce a new sub-linear space sketch—the Weight-Median Sketch—for learning compressed linear classifiers over data streams while supporting the efficient recovery of large-magnitude weights in the model. This enables memory-limited execution of several statistical analyses over streams, including online feature selection, streaming data explanation, relative deltoid detection, and streaming estimation of pointwise mutual information. Unlike related sketches that capture the most frequently-occurring features (or items) in a data stream, the Weight-Median Sketch captures the features that are most discriminative of one stream (or class) compared to another. The Weight-Median Sketch adopts the core data structure used in the Count-Sketch, but, instead of sketching counts, it captures sketched gradient updates to the model parameters. We provide a theoretical analysis that establishes recovery guarantees for batch and online learning, and demonstrate empirical improvements in memory-accuracy trade-offs over alternative memory-budgeted methods, including count-based sketches and feature hashing.
Traditional security measures for large-scale critical infrastructure systems have focused on keeping adversaries out of the system. As the Internet of Things (IoT) extends into millions of homes, with tens or hundreds of devices each, the threat landscape is complicated. IoT devices have unknown access capabilities with unknown reach into other systems. This paper presents ongoing work on how techniques in sensor verification and cyber-physical modeling and analysis on bulk power systems can be applied to identify malevolent IoT devices and secure smart and connected communities against the most impactful threats.
In blockchain-based systems, malicious behaviour can be detected using auditable information in transactions managed by distributed ledgers. Besides cryptocurrency, blockchain technology has recently been used for other applications, such as file storage. However, most of existing blockchain- based file storage systems can not revoke a user efficiently when multiple users have access to the same file that is encrypted. Actually, they need to update file encryption keys and distribute new keys to remaining users, which significantly increases computation and bandwidth overheads. In this work, we propose a blockchain and proxy re-encryption based design for encrypted file sharing that brings a distributed access control and data management. By combining blockchain with proxy re-encryption, our approach not only ensures confidentiality and integrity of files, but also provides a scalable key management mechanism for file sharing among multiple users. Moreover, by storing encrypted files and related keys in a distributed way, our method can resist collusion attacks between revoked users and distributed proxies.
Traffic state estimation helps urban traffic control and management. In this paper, a traffic state estimation model based on the fusion of Hidden Markov model and SEA algorithm is proposed considering the randomness and volatility of traffic systems. Traffic data of average travel speed in selected city were collected, and the mean and fluctuation values of average travel speed in adjacent time windows were calculated. With Hidden Markov model, the system state network is defined according to mean values and fluctuation values. The operation efficiency of traffic system, as well as stability and trend values, were calculated with System Effectiveness Analysis (SEA) algorithm based on system state network. Calculation results show that the method perform well and can be applied to both traffic state assessment of certain road sections and large scale road networks.
With the vision of building "A Smart World", Internet of Things (IoT) plays a crucial role where users, computing systems and objects with sensing and actuating capabilities cooperate with unparalleled convenience. Among many applications of IoT, healthcare is the most emerging in today's scenario, as new technological advancement creates opportunity for early detection of illnesses, quick decision generation and even aftercare monitoring. Nowadays, it has become a reality for many patients to be monitored remotely, overcoming traditional logistical obstacles. However, these e-health applications increase the concerns of security, privacy, and integrity of medical data. For secured transmission in IoT healthcare, data that has been gathered from sensors in a patient's body area network needs to be sent to the end user and might need to be aggregated, visualized and/or evaluated before being presented. Here, trust is critical. Therefore, an end-to-end trustworthy system architecture can guarantee the reliable transmission of a patient's data and confirms the success of IoT Healthcare application.
Decision making in utilities, municipal, and energy companies depends on accurate and trustworthy weather information and predictions. Recently, crowdsourced personal weather stations (PWS) are being increasingly used to provide a higher spatial and temporal resolution of weather measurements. However, tools and methods to ensure the trustworthiness of the crowdsourced data in real-time are lacking. In this paper, we present a Reputation System for Crowdsourced Rainfall Networks (RSCRN) to assign trust scores to personal weather stations in a region. Using real PWS data from the Weather Underground service in the high flood risk region of Norfolk, Virginia, we evaluate the performance of the proposed RSCRN. The proposed method is able to converge to a confident trust score for a PWS within 10–20 observations after installation. Collectively, the results indicate that the trust score derived from the RSCRN can reflect the collective measure of trustworthiness to the PWS, ensuring both useful and trustworthy data for modeling and decision-making in the future.
The need to process the verity, volume and velocity of data generated by today's Internet of Things (IoT) devices has pushed both academia and the industry to investigate new architectural alternatives to support the new challenges. As a result, Edge Computing (EC) has emerged to address these issues, by placing part of the cloud resources (e.g., computation, storage, logic) closer to the edge of the network, which allows faster and context dependent data analysis and storage. However, as EC infrastructures grow, different providers who do not necessarily trust each other need to collaborate in order serve different IoT devices. In this context, EC infrastructures, IoT devices and the data transiting the network all need to be subject to identity and provenance checks, in order to increase trust and accountability. Each device/data in the network needs to be identified and the provenance of its actions needs to be tracked. In this paper, we propose a blockchain container based architecture that implements the W3C-PROV Data Model, to track identities and provenance of all orchestration decisions of a business network. This architecture provides new forms of interaction between the different stakeholders, which supports trustworthy transactions and leads to a new decentralized interaction model for IoT based applications.
Transition noise and remanence noise are the two most important types of media noise in heat-assisted magnetic recording. We examine two methods (spatial splitting and principal components analysis) to distinguish them: both techniques show similar trends with respect to applied field and grain pitch (GP). It was also found that PW50can be affected by GP and reader design, but is almost independent of write field and bit length (larger than 50 nm). Interestingly, our simulation shows a linear relationship between jitter and PW50NSRrem, which agrees qualitatively with experimental results.
This paper presents a novel Kriged Compressive Sensing (KCS) approach for the reconstruction of underwater acoustic intensity fields sampled by multiple gliders following sawtooth sampling patterns. Blank areas in between the sampling trajectories may cause unsatisfying reconstruction results. The KCS method leverages spatial statistical correlation properties of the acoustic intensity field being sampled to improve the compressive reconstruction process. Virtual data samples generated from a kriging method are inserted into the blank areas. We show that by using the virtual samples along with real samples, the acoustic intensity field can be reconstructed with higher accuracy when coherent spatial patterns exist. Corresponding algorithms are developed for both unweighted and weighted KCS methods. By distinguishing the virtual samples from real samples through weighting, the reconstruction results can be further improved. Simulation results show that both algorithms can improve the reconstruction results according to the PSNR and SSIM metrics. The methods are applied to process the ocean ambient noise data collected by the Sea-Wing acoustic gliders in the South China Sea.
Virtual Machine Introspection (VMI) is an emerging family of techniques for extracting data from virtual machines without the use of active monitoring probes within the target machines themselves. In VMI based systems, the data is collected at the hypervisor-level by analyzing the state of virtual machines. This has the benefit of making collection harder to detect and block by malware as there is nothing in the machine indicating that monitoring is taking place. In this paper we present Nitro Web, a web-based monitoring system for virtual machines that uses virtual machine introspection for data collection. The platform is capable of detecting and visualizing system call activity taking place within virtual machines in real-time. The secondary purpose of this paper is to offer an introduction to Nitro virtual machine introspection framework that we have been involved in developing. In this paper, we reflect on how Nitro Framework can be used for building applications making use of VMI data.
In this paper, we propose a novel visual secret sharing (VSS) scheme for color QR code (VSSCQR) with (n, n) threshold based on high capacity, admirable visual effects and popularity of color QR code. By splitting and encoding a secret image into QR codes and then fusing QR codes to generate color QR code shares, the scheme can share the secret among a certain number of participants. However, less than n participants cannot reveal any information about the secret. The embedding amount and position of the secret image bits generated by VSS are in the range of the error correction ability of the QR code. Each color share is readable, which can be decoded and thus may not come into notice. On one hand, the secret image can be reconstructed by first decomposing three QR codes from each color QR code share and then stacking the corresponding QR codes based on only human visual system without computational devices. On the other hand, by decomposing three QR codes from each color QR code share and then XORing the three QR codes respectively, we can reconstruct the secret image losslessly. The experiment results display the effect of our scheme.
Cyber physical systems are the key innovation driver for many domains such as automotive, avionics, industrial process control, and factory automation. However, their interconnection potentially provides adversaries easy access to sensitive data, code, and configurations. If attackers gain control, material damage or even harm to people must be expected. To counteract data theft, system manipulation and cyber-attacks, security mechanisms must be embedded in the cyber physical system. Adding hardware security in the form of the standardized Trusted Platform Module (TPM) is a promising approach. At the same time, traditional dependability features such as safety, availability, and reliability have to be maintained. To determine the right balance between security and dependability it is essential to understand their interferences. This paper supports developers in identifying the implications of using TPMs on the dependability of their system.We highlight potential consequences of adding TPMs to cyber-physical systems by considering the resulting safety, reliability, and availability. Furthermore, we discuss the potential of enhancing the dependability of TPM services by applying traditional redundancy techniques.
Healthcare is a vital component of every nation's critical infrastructure, yet it is one of the most vulnerable sector for cyber-attacks. To enforce the knowledge on information security processes and data protection procedures, educational and training schemes should be establishedfor information technology (IT) staff working in healthcare settings. However, only training IT staff is not enough, as many of cybersecurity threats are caused by human errors or lack of awareness. Current awareness and training schemes are often implemented in silos, concentrating on one aspect of cybersecurity at a time. Proactive Resilience Educational Framework (Prosilience EF) provides a holistic cyber resilience and security framework for developing and delivering a multilateral educational and training scheme based on a proactive approach to cybersecurity. The framework is built on the principle that education and training must be interactive, guided, meaningful and directly relevant to the user' operational environment. The framework addresses capacity mapping, cyber resilience level measuring, utilizing available and mapping missing resources, adaptive learning technologies and dynamic content delivery. Prosilience EF launches an iterative process of awareness and training development with relevant stakeholders (end users - hospitals, healthcare authorities, cybersecurity training providers, industry members), evaluating the framework via joint exercises/workshops andfurther developing the framework.
This project develops techniques to protect against sensor attacks on cyber-physical systems. Specifically, a resilient version of the Kalman filtering technique accompanied with a watermarking approach is proposed to detect cyber-attacks and estimate the correct state of the system. The defense techniques are used in conjunction and validated on two case studies: i) an unmanned ground vehicle (UGV) in which an attacker alters the reference angle and ii) a Cube Satellite (CubeSat) in which an attacker modifies the orientation of the satellite degrading its performance. Based on this work, we show that the proposed techniques in conjunction achieve better resiliency and defense capability than either technique alone against spoofing and replay attacks.