When people utilize social applications and services, their privacy suffers a potential serious threat. In this article, we present a novel, robust, and effective de-anonymization attack to mobility trace data and social data. First, we design a Unified Similarity (US) measurement, which takes account of local and global structural characteristics of data, information obtained from auxiliary data, and knowledge inherited from ongoing de-anonymization results. By analyzing the measurement on real datasets, we find that some data can potentially be de-anonymized accurately and the other can be de-anonymized in a coarse granularity. Utilizing this property, we present a US-based De-Anonymization (DA) framework, which iteratively de-anonymizes data with accuracy guarantee. Then, to de-anonymize large-scale data without knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework. By smartly working on two core matching subgraphs , ADA achieves high de-anonymization accuracy and reduces computational overhead. Finally, we examine the presented de-anonymization attack on three well-known mobility traces: St Andrews, Infocom06, and Smallblue, and three social datasets: ArnetMiner, Google+, and Facebook. The experimental results demonstrate that the presented de-anonymization framework is very effective and robust to noise. The source code and employed datasets are now publicly available at SecGraph [2015].

In the attempt to bring modern broadband Internet features to traditional broadcast television, the Digital Video Broadcasting (DVB) consortium introduced a specification called Hybrid Broadcast-Broadband Television (HbbTV), which allows broadcast streams to include embedded HTML content that is rendered by the television. This system is already in very wide deployment in Europe and has recently been adopted as part of the American digital television standard. Our analyses of the specifications, and of real systems implementing them, show that the broadband and broadcast systems are combined insecurely. This enables a large-scale exploitation technique with a localized geographical footprint based on Radio Frequency (RF) injection, which requires a minimal budget and infrastructure and is remarkably difficult to detect. In this article, we present the attack methodology and a number of follow-on exploitation techniques that provide significant flexibility to attackers. Furthermore, we demonstrate that the technical complexity and required budget are low, making this attack practical and realistic, especially in areas with high population density: In a dense urban area, an attacker with a budget of about 450 can target more than 20,000 devices in a single attack. A unique aspect of this attack is that, in contrast to most Internet of Things/Cyber-Physical System threat scenarios, where the attack comes from the data network side and affects the physical world, our attack uses the physical broadcast network to attack the data network.

The Internet provides an ideal anonymous channel for concealing computer-mediated malicious activities, as the network-based origins of critical electronic textual evidence (e.g., emails, blogs, forum posts, chat logs, etc.) can be easily repudiated. Authorship attribution is the study of identifying the actual author of the given anonymous documents based on the text itself, and for decades, many linguistic stylometry and computational techniques have been extensively studied for this purpose. However, most of the previous research emphasizes promoting the authorship attribution accuracy, and few works have been done for the purpose of constructing and visualizing the evidential traits. In addition, these sophisticated techniques are difficult for cyber investigators or linguistic experts to interpret. In this article, based on the End-to-End Digital Investigation (EEDI) framework, we propose a visualizable evidence-driven approach, namely VEA, which aims at facilitating the work of cyber investigation. Our comprehensive controlled experiment and the stratified experiment on the real-life Enron email dataset demonstrate that our approach can achieve even higher accuracy than traditional methods; meanwhile, its output can be easily visualized and interpreted as evidential traits. In addition to identifying the most plausible author of a given text, our approach also estimates the confidence for the predicted result based on a given identification context and presents visualizable linguistic evidence for each candidate.

Users often wish to communicate anonymously on the Internet, for example, in group discussion or instant messaging forums. Existing solutions are vulnerable to misbehaving users, however, who may abuse their anonymity to disrupt communication. Dining Cryptographers Networks (DC-nets) leave groups vulnerable to denial-of-service and Sybil attacks; mix networks are difficult to protect against traffic analysis; and accountable voting schemes are unsuited to general anonymous messaging. dissent is the first general protocol offering provable anonymity and accountability for moderate-size groups, while efficiently handling unbalanced communication demands among users. We present an improved and hardened dissent protocol, define its precise security properties, and offer rigorous proofs of these properties. The improved protocol systematically addresses the delicate balance between provably hiding the identities of well-behaved users, while provably revealing the identities of disruptive users, a challenging task because many forms of misbehavior are inherently undetectable. The new protocol also addresses several nontrivial attacks on the original dissent protocol stemming from subtle design flaws.

(U.S.) Rule-based policies for mitigating software risk suggest using the CVSS score to measure the risk of an individual vulnerability and act accordingly. A key issue is whether the ‘danger’ score does actually match the risk of exploitation in the ...

Cloud computing services are becoming more prevalent and readily available today, bringing to us economies of scale and making large-scale computation feasible. Security and privacy considerations, however, stand in the way of fully utilizing the benefits of such services and architectures. In this work we address the problem of secure outsourcing of large-scale biometric experiments to a cloud or grid in a way that the client can verify that with very high probability the task was computed correctly. We conduct thorough theoretical analysis of the proposed techniques and provide implementation results that indicate that our solution imposes modest overhead.

We introduce a new practical mechanism for remote data storage with access pattern privacy and correctness . A storage client can deploy this mechanism to issue encrypted reads, writes, and inserts to a potentially curious and malicious storage service provider, without revealing information or access patterns. The provider is unable to establish any correlation between successive accesses, or even to distinguish between a read and a write. Moreover, the client is provided with strong correctness assurances for its operations—illicit provider behavior does not go undetected. We describe a practical system that can execute an unprecedented several queries per second on terabyte-plus databases while maintaining full computational privacy and correctness.

For performance and for incorporating legacy libraries, many Java applications contain native-code components written in unsafe languages such as C and C++. Native-code components interoperate with Java components through the Java Native Interface (JNI). As native code is not regulated by Java's security model, it poses serious security threats to the managed Java world. We introduce a security framework that extends Java's security model and brings native code under control. Leveraging software-based fault isolation, the framework puts native code in a separate sandbox and allows the interaction between the native world and the Java world only through a carefully designed pathway. Two different implementations were built. In one implementation, the security framework is integrated into a Java Virtual Machine (JVM). In the second implementation, the framework is built outside of the JVM and takes advantage of JVM-independent interfaces. The second implementation provides JVM portability, at the expense of some performance degradation. Evaluation of our framework demonstrates that it incurs modest runtime overhead while significantly enhancing the security of Java applications.

Most commodity peripheral devices and their drivers are geared to achieve high performance with security functions being opted out. The absence of strong security measures invites attacks on the I/O data and consequently posts threats to those services feeding on them, such as fingerprint-based biometric authentication. In this article, we present a generic solution called DriverGuard, which dynamically protects the secrecy of I/O flows such that the I/O data are not exposed to the malicious kernel. Our design leverages a composite of cryptographic and virtualization techniques to achieve fine-grained protection without using any extra devices and modifications on user applications. We implement the DriverGuard prototype on Xen by adding around 1.7K SLOC. DriverGuard is lightweight as it only needs to protect around 2% of the driver code’s execution. We measure the performance and evaluate the security of DriverGuard with three input devices (keyboard, fingerprint reader and camera) and three output devices (printer, graphic card, and sound card). The experiment results show that DriverGuard induces negligible overhead to the applications.

Code Pointer Masking (CPM) is a novel countermeasure against code injection attacks on native code. By enforcing the correct semantics of code pointers, CPM thwarts attacks that modify code pointers to divert the application’s control flow. It does not ...