Biblio
Popular anonymity mechanisms such as Tor provide low communication latency but are vulnerable to traffic analysis attacks that can de-anonymize users. Moreover, known traffic-analysis-resistant techniques such as Dissent are impractical for use in latency-sensitive settings such as wireless networks. In this paper, we propose PriFi, a low-latency protocol for anonymous communication in local area networks that is provably secure against traffic analysis attacks. This allows members of an organization to access the Internet anonymously while they are on-site, via privacy-preserving WiFi networking, or off-site, via privacy-preserving virtual private networking (VPN). PriFi reduces communication latency using a client/relay/server architecture in which a set of servers computes cryptographic material in parallel with the clients to minimize unnecessary communication latency. We also propose a technique for protecting against equivocation attacks, with which a malicious relay might de-anonymize clients. This is achieved without adding extra latency by encrypting client messages based on the history of all messages they have received so far. As a result, any equivocation attempt makes the communication unintelligible, preserving clients' anonymity while holding the servers accountable.
Contactless communications have become omnipresent in our daily lives, from simple access cards to electronic passports. Such systems are particularly vulnerable to relay attacks, in which an adversary relays the messages from a prover to a verifier. Distance-bounding protocols were introduced to counter such attacks. Lately, there has been a very active research trend on improving the security of these protocols, but also on ensuring strong privacy properties with respect to active adversaries and malicious verifiers. In particular, a difficult threat to address is the terrorist fraud, in which a far-away prover cooperates with a nearby accomplice to fool a verifier. The usual defence against this attack is to make it impossible for the accomplice to succeed unless the prover provides him with enough information to recover his secret key and impersonate him later on. However, the mere existence of a long-term secret key is problematic with respect to privacy. In this paper, we propose a novel approach in which the prover does not leak his secret key but a reusable session key along with a group signature on it. This allows the adversary to impersonate him even without knowing his signature key. Based on this approach, we give the first distance-bounding protocol, called SPADE, integrating anonymity, revocability and provable resistance to standard threat models.
Presented at NSA SoS Quarterly Meeting, February 2, 2017.
We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.
Recent advances in vehicle automation have led to excitement and discourse in academia, industry, the media, and the public. Human factors such as trust and user experience are critical in terms of safety and customer acceptance. One of the main challenges in partial and conditional automation is related to drivers' situational awareness, or a lack thereof. In this paper, we critically analyse state of the art implementations in this arena and present a proactive approach to increasing situational awareness. We propose to make use of augmented reality to carefully design applications aimed at constructs such as amplification and voluntary attention. Finally, we showcase an example application, Pokémon DRIVE, that illustrates the utility of our proposed approach.
Named-Data Networking (NDN) is the most prominent proposal for a clean-slate proposal of Future Internet. Nevertheless, NDN routing schemes present scalability concerns due to the required number of stored routes and of control messages. In this work, we present a controller-based routing protocol using a formal method to unambiguously specify, and validate to prove its correctness. Our proposal codes signaling information on content names, avoiding control message overhead, and reduces router memory requirements, storing only the routes for simultaneously consumed prefixes. Additionally, the protocol installs a new route on all routers in a path with a single route request to the controller, avoiding replication of routing information and automating router provisioning. As a result, we provide a protocol proposal description using the Specification and Description Language and we validate the protocol, proving that CRoS behavior is free of dead or live locks. Furthermore, the protocol validation guarantees that the scheme ensures a valid working path from consumer to producer, even if it does not assure the shortest path.
Android applications pose security and privacy risks for end-users. These risks are often quantified by performing dynamic analysis and permission analysis of the Android applications after release. Prediction of security and privacy risks associated with Android applications at early stages of application development, e.g. when the developer (s) are
writing the code of the application, might help Android application developers in releasing applications to end-users that have less security and privacy risk. The goal of this paper
is to aid Android application developers in assessing the security and privacy risk associated with Android applications by using static code metrics as predictors. In our paper, we consider security and privacy risk of Android application as how susceptible the application is to leaking private information of end-users and to releasing vulnerabilities. We investigate how effectively static code metrics that are extracted from the source code of Android applications, can be used to predict security and privacy risk of Android applications. We collected 21 static code metrics of 1,407 Android applications, and use the collected static code metrics to predict security and privacy risk of the applications. As the oracle of security and privacy risk, we used Androrisk, a tool that quantifies the amount of security and privacy risk of an Android application using analysis of Android permissions and dynamic analysis. To accomplish our goal, we used statistical learners such as, radial-based support vector machine (r-SVM). For r-SVM, we observe a precision of 0.83. Findings from our paper suggest that with proper selection of static code metrics, r-SVM can be used effectively to predict security and privacy risk of Android applications
Physical layer security for wireless communication is broadly considered as a promising approach to protect data confidentiality against eavesdroppers. However, despite its ample theoretical foundation, the transition to practical implementations of physical-layer security still lacks success. A close inspection of proven vulnerable physical-layer security designs reveals that the flaws are usually overlooked when the scheme is only evaluated against an inferior, single-antenna eavesdropper. Meanwhile, the attacks exposing vulnerabilities often lack theoretical justification. To reduce the gap between theory and practice, we posit that a physical-layer security scheme must be studied under multiple adversarial models to fully grasp its security strength. In this regard, we evaluate a specific physical-layer security scheme, i.e. orthogonal blinding, under multiple eavesdropper settings. We further propose a practical "ciphertext-only attack" that allows eavesdroppers to recover the original message by exploiting the low entropy fields in wireless packets. By means of simulation, we are able to reduce the symbol error rate at an eavesdropper below 1% using only the eavesdropper's receiving data and a general knowledge about the format of the wireless packets.
Given a set D of tuples defined on a domain Omega, we study differentially private algorithms for constructing a histogram over Omega to approximate the tuple distribution in D. Existing solutions for the problem mostly adopt a hierarchical decomposition approach, which recursively splits Omega into sub-domains and computes a noisy tuple count for each sub-domain, until all noisy counts are below a certain threshold. This approach, however, requires that we (i) impose a limit h on the recursion depth in the splitting of Omega and (ii) set the noise in each count to be proportional to h. The choice of h is a serious dilemma: a small h makes the resulting histogram too coarse-grained, while a large h leads to excessive noise in the tuple counts used in deciding whether sub-domains should be split. Furthermore, h cannot be directly tuned based on D; otherwise, the choice of h itself reveals private information and violates differential privacy. To remedy the deficiency of existing solutions, we present PrivTree, a histogram construction algorithm that adopts hierarchical decomposition but completely eliminates the dependency on a pre-defined h. The core of PrivTree is a novel mechanism that (i) exploits a new analysis on the Laplace distribution and (ii) enables us to use only a constant amount of noise in deciding whether a sub-domain should be split, without worrying about the recursion depth of splitting. We demonstrate the application of PrivTree in modelling spatial data, and show that it can be extended to handle sequence data (where the decision in sub-domain splitting is not based on tuple counts but a more sophisticated measure). Our experiments on a variety of real datasets show that PrivTree considerably outperforms the states of the art in terms of data utility.
Privacy-preserving range queries allow encrypting data while still enabling queries on ciphertexts if their corresponding plaintexts fall within a requested range. This provides a data owner the possibility to outsource data collections to a cloud service provider without sacrificing privacy nor losing functionality of filtering this data. However, existing methods for range queries either leak additional information (like the ordering of the complete data set) or slow down the search process tremendously by requiring to query each ciphertext in the data collection. We present a novel scheme that only leaks the access pattern while supporting amortized poly-logarithmic search time. Our construction is based on the novel idea of enabling the cloud service provider to compare requested range queries. By doing so, the cloud service provider can use the access pattern to speed-up search time for range queries in the future. On the one hand, values that have fallen within a queried range, are stored in an interactively built index for future requests. On the other hand, values that have not been queried do not leak any information to the cloud service provider and stay perfectly secure. In order to show its practicability we have implemented our scheme and give a detailed runtime evaluation.
Recently, code reuse attacks (CRAs) have emerged as a new class of ingenious security threatens. Attackers can utilize CRAs to hijack the control flow of programs to perform malicious actions without injecting any codes. Existing defenses against CRAs often incur high memory and performance overheads or require extending the existing processors' instruction set architectures (ISAs). To tackle these issues, we propose a hardware-based control flow integrity (CFI) that employs physical unclonable functions (PUF)-based linear encryption architecture (LEA) to protect against CRAs with negligible hardware extending and run time overheads. The proposed method can protect ret and indirect jmp instructions from return oriented programming (ROP) and jump oriented programming (JOP) without any additional software manipulations and extending ISAs. The pre-process will be conducted on codes once the executable binary is loaded into memory, and the real-time control flow verification based on LEA can be done while ret and jmp instructions are executed. Performance evaluations on benchmarks show that the proposed method only introduces 0.61% run-time overhead and 0.63% memory overhead on average.
Modern operating systems use hardware support to protect against control-flow hijacking attacks such as code-injection attacks. Typically, write access to executable pages is prevented and kernel mode execution is restricted to kernel code pages only. However, current CPUs provide no protection against code-reuse attacks like ROP. ASLR is used to prevent these attacks by making all addresses unpredictable for an attacker. Hence, the kernel security relies fundamentally on preventing access to address information. We introduce Prefetch Side-Channel Attacks, a new class of generic attacks exploiting major weaknesses in prefetch instructions. This allows unprivileged attackers to obtain address information and thus compromise the entire system by defeating SMAP, SMEP, and kernel ASLR. Prefetch can fetch inaccessible privileged memory into various caches on Intel x86. It also leaks the translation-level for virtual addresses on both Intel x86 and ARMv8-A. We build three attacks exploiting these properties. Our first attack retrieves an exact image of the full paging hierarchy of a process, defeating both user space and kernel space ASLR. Our second attack resolves virtual to physical addresses to bypass SMAP on 64-bit Linux systems, enabling ret2dir attacks. We demonstrate this from unprivileged user programs on Linux and inside Amazon EC2 virtual machines. Finally, we demonstrate how to defeat kernel ASLR on Windows 10, enabling ROP attacks on kernel and driver binary code. We propose a new form of strong kernel isolation to protect commodity systems incuring an overhead of only 0.06-5.09%.
Blind signature can be deployed to preserve user anonymity and is widely used in digital cash and e-voting. As an interactive protocol, blind signature schemes require high efficiency. In this paper, we propose a code-based blind signature scheme with high efficiency as it can produce a valid signature without many loops unlike existing code-based signature schemes. We then prove the security of our scheme in the random oracle model and analyze the efficiency of our scheme. Since a code-based signature scheme is post-quantum cryptography, therefore, the scheme is also able to resist quantum attacks.
Blind signature can be deployed to preserve user anonymity and is widely used in digital cash and e-voting. As an interactive protocol, blind signature schemes require high efficiency. In this paper, we propose a code-based blind signature scheme with high efficiency as it can produce a valid signature without many loops unlike existing code-based signature schemes. We then prove the security of our scheme in the random oracle model and analyze the efficiency of our scheme. Since a code-based signature scheme is post-quantum cryptography, therefore, the scheme is also able to resist quantum attacks.
Biometric verification systems have security issues regarding the storage of biometric data in that a user's biometric features cannot be changed into other ones even when a system is compromised. To address this issue, it may be safe to store the biometrics data on a reliable remote server instead of storing them in a local device. However, this approach may raise a privacy issue. In this paper, we propose a biometric verification system where the biometric data are stored in a remote server in an encrypted form and the similarity of the user input to the registered biometric data is computed in an encrypted domain using a homomorphic encryption. We evaluated the performance of the proposed system through an implementation on an Android-based smartphone and an i7-based server.
The concept of digital right management (DRM) has become extremely important in current mobile environments. This paper shows how partial bitstream encryption can allow the secure distribution of hardware applications resembling the mechanisms of traditional software DRM. Building on the recent developments towards the secure distribution of hardware cores, the paper demonstrates a prototypical implementation of a user mobile device supporting such distribution mechanisms. The prototype extends the Android operating system with support for hardware reconfigurability and showcases the interplay of novel security concepts enabled by hardware DRM, the advantages of a design flow based on high-level synthesis, and the opportunities provided by current software-rich reconfigurable Systems-on-Chips. Relying on this prototype, we also collected extensive quantitative results demonstrating the limited overhead incurred by the secure distribution architecture.
The majority of applications use a prompt for a username and password. Passwords are recommended to be unique, long, complex, alphanumeric and non-repetitive. These reasons that make passwords secure may prove to be a point of weakness. The complexity of the password provides a challenge for a user and they may choose to record it. This compromises the security of the password and takes away its advantage. An alternate method of security is Keystroke Biometrics. This approach uses the natural typing pattern of a user for authentication. This paper proposes a new method for reducing error rates and creating a robust technique. The new method makes use of multiple sensors to obtain information about a user. An artificial neural network is used to model a user's behavior as well as for retraining the system. An alternate user verification mechanism is used in case a user is unable to match their typing pattern.
In this paper we use car games as a simulator for real automobiles, and generate driving logs that contain the vehicle data. This includes values for parameters like gear used, speed, left turns taken, right turns taken, accelerator, braking and so on. From these parameters we have derived some more additional parameters and analyzed them. As the input from automobile driver is only routine driving, no explicit feedback is required; hence there are more chances of being able to accurately profile the driver. Experimentation and analysis from this logged data shows possibility that driver profiling can be done from vehicle data. Since the profiles are unique, these can be further used for a wide range of applications and can successfully exhibit typical driving characteristics of each user.
Intellectual Property (IP) verification is a crucial component of System-on-Chip (SoC) design in the modern IC design business model. Given a globalized supply chain and an increasing demand for IP reuse, IP theft has become a major concern for the IC industry. In this paper, we address the trust issues that arise between IP owners and IP users during the functional verification of an IP core. Our proposed scheme ensures the privacy of IP owners and users, by a) generating a privacy-preserving version of the IP, which is functionally equivalent to the original design, and b) employing homomorphically encrypted input vectors. This allows the functional verification to be securely outsourced to a third-party, or to be executed by either parties, while revealing the least possible information regarding the test vectors and the IP core. Experiments on both combinational and sequential benchmark circuits demonstrate up to three orders of magnitude IP verification slowdown, due to the computationally intensive fully homomorphic operations, for different security parameter sizes.
The energy sector has been actively looking into cyber risk assessment at a global level, as it has a ripple effect; risk taken at one step in supply chain has an impact on all the other nodes. Cyber-attacks not only hinder functional operations in an organization but also waves damaging effects to the reputation and confidence among shareholders resulting in financial losses. Organizations that are open to the idea of protecting their assets and information flow and are equipped; enough to respond quickly to any cyber incident are the ones who prevail longer in global market. As a contribution we put forward a modular plan to mitigate or reduce cyber risks in global supply chain by identifying potential cyber threats at each step and identifying their immediate counterm easures.
The Internet of Things (IoT), an emerging global network of uniquely identifiable embedded computing devices within the existing Internet infrastructure, is transforming how we live and work by increasing the connectedness of people and things on a scale that was once unimaginable. In addition to increased communication efficiency between connected objects, the IoT also brings new security and privacy challenges. Comprehensive measures that enable IoT device authentication and secure access control need to be established. Existing hardware, software, and network protection methods, however, are designed against fraction of real security issues and lack the capability to trace the provenance and history information of IoT devices. To mitigate this shortcoming, we propose an RFID-enabled solution that aims at protecting endpoint devices in IoT supply chain. We take advantage of the connection between RFID tag and control chip in an IoT device to enable data transfer from tag memory to centralized database for authentication once deployed. Finally, we evaluate the security of our proposed scheme against various attacks.
The safety, security, and resilience of international postal, shipping, and transportation critical infrastructure are vital to the global supply chain that enables worldwide commerce and communications. But security on an international scale continues to fail in the face of new threats, such as the discovery by Panamanian authorities of suspected components of a surface-to-air missile system aboard a North Korean-flagged ship in July 2013 [1].This reality calls for new and innovative approaches to critical infrastructure security. Owners and operators of critical postal, shipping, and transportation operations need new methods to identify, assess, and mitigate security risks and gaps in the most effective manner possible.
The main emphasis of this paper is to develop an approach able to detect and assess blindly the perceptual blur degradation in images. The idea deals with a statistical modelling of perceptual blur degradation in the frequency domain using the discrete cosine transform (DCT) and the Just Noticeable Blur (JNB) concept. A machine learning system is then trained using the considered statistical features to detect perceptual blur effect in the acquired image and eventually produces a quality score denoted BBQM for Blind Blur Quality Metric. The proposed BBQM efficiency is tested objectively by evaluating it's performance against some existing metrics in terms of correlation with subjective scores.
Image Denoising nowadays is a great Challenge in the field of image processing. Since Discrete wavelet transform (DWT) is one of the powerful and perspective approaches in the area of image de noising. But fixing an optimal threshold is the key factor to determine the performance of denoising algorithm using (DWT). The optimal threshold can be estimated from the image statistics for getting better performance of denoising in terms of clarity or quality of the images. In this paper we analyzed various methods of denoising from the sonar image by using various thresholding methods (Vishnu Shrink, Bayes Shrink and Neigh Shrink) experimentally and compare the result in terms of various image quality parameters. (PSNR,MSE,SSIM and Entropy). The results of the proposed method show that there is an improvenment in the visual quality of sonar images by suppressing the speckle noise and retaining edge details.