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Traffic monitoring services collect traffic reports and respond to users’ traffic queries. However, the reports and queries may reveal the user’s identity and location. Although different anonymization techniques have been applied to protect user privacy, a new security threat arises, namely, n-by-1 jamming attack, in which an anonymous contributing driver impersonates $n$ drivers and uploads $n$ normal reports by using $n$ reporting devices. Such an attack will mislead the traffic monitoring service provider and further degrade the service quality. Existing traffic monitoring services do not support customized queries, and private information retrieval techniques cannot be applied directly in traffic monitoring. We formally define the new attack and propose a traffic monitoring scheme TraJ to defend the attack and achieve user-defined location privacy. Specifically, we bridge anonymous contributing drivers without disclosing their speed set by using private set intersection. Each RSU collects time traffic reports and structures a weighted proximity graph to filter out malicious colluding drivers. We design a user-defined privacy-preserving query method by encoding complex road network. We leverage the uploading phase from private aggregation to collect traffic conditions and allow requesting drivers to dynamically and privately query traffic conditions. We provide a formal analysis of TraJ to prove its privacy and security properties. We also construct a prototype based on a real-world dataset and Android smartphones to demonstrate its feasibility and efficiency. A formal analysis demonstrates the privacy and security properties. Extensive experiments illustrate the performance and defense efficacy. [ABSTRACT FROM AUTHOR]

E-commerce platforms incorporate reputation systems that allow customers to rate suppliers following financial transactions. Existing reputation systems cannot defend the centralized server against arbitrarily tampering with the supplier’s reputation. Furthermore, they do not offer reputation access across platforms. Rates are faced with privacy leakages because rating activities are correlated with privacy (e.g., identity and rating). Meanwhile, raters could be malicious and initiate multiple rating attacks and abnormal rating attacks. Determining how to address these issues have both research and practical value. In this paper, we propose a blockchain-based privacy-preserving reputation system for e-commerce platforms named RepChain; our system allows cross-platform reputation access and anonymous and private ratings. Using RepChain, all e-commerce platforms collaborate and share users’ reputations by co-constructing a consortium blockchain and modeling the rating process as a finite state machine. In particular, we facilitate one-show anonymous credentials constructed from two-move blind signatures to protect customers’ identities and resist multiple rating attacks, leverage zero-knowledge range proof to verify the correctness of ratings and defend against abnormal rating attacks, design a secure sum computation protocol among nodes to update reputations, and verify ratings via batch processing and consensus hashes. Finally, we demonstrate the security and privacy of RepChain via a formal analysis and evaluate its performance based on Ethereum test network. [ABSTRACT FROM AUTHOR]

Energy trading in Industrial Internet of Things (IIoT), a fundamental approach to realize Industry 4.0, plays a vital role in satisfying energy demands and optimizing system efficiency. Existing research works utilize a utility company to distribute energy to energy nodes with the help of energy brokers. Afterwards, they apply blockchain to provide transparency, immutability, and auditability of peer-to-peer (P2P) energy trading. However, their schemes are constructed on a weak security model and do not consider the cheating attack initiated by energy sellers. Such an attack refers to an energy seller refusing to transfer the negotiated energy to an energy purchaser who already paid money. In this article, we propose $\mathsf{FeneChain}$ , a blockchain-based energy trading scheme to supervise and manage the energy trading process toward building a secure energy trading system and improving energy quality for Industry 4.0. Specifically, we leverage anonymous authentication to protect user privacy, and we design a timed-commitments-based mechanism to guarantee the verifiable fairness during energy trading. Moreover, we utilize fine-grained access control for energy trading services. We also build a consortium blockchain among energy brokers to verify and record energy trading transactions. Finally, we formally analyze the security and privacy of $\mathsf{FeneChain}$ and evaluate its performance (i.e., computational costs and communication overhead) by implementing a prototype via a local Ethereum test network and Raspberry Pi. [ABSTRACT FROM AUTHOR]

Network traffic classification finds its applications in a variety of network management tasks such as quality of service, security monitoring, traffic engineering, etc. Deep Packet Inspection is one of the methods to identify applications. With the number of proprietary protocols on the rise and network protocols using bit level information for encoding, recently it has been shown that bit level signatures are effective for identifying applications. In this paper, we propose BitProb which generates probabilistic bit signatures for traffic classification. It uses the probability of a bit at a particular position being either 0 or 1 and generates a space efficient signature represented as a state transition machine. Subsequently, it uses the overall probability of an ${n}$ bit binary string extracted from a network flow to identify which application generated the flow. We experiment with three datasets covering twenty protocols (text, binary and proprietary) and show that BitProb classifies network flows with high accuracy and has a minimum number of misclassifications. [ABSTRACT FROM AUTHOR]

Various sources and sophisticated tools are used to gather and process the comparatively large volume of data or big data that sometimes leads to privacy disclosure (at broader or finer level) for the data owner. Privacy preserving data publishing approaches such as k-anonymity, l-diversity, and t-closeness are very well used to de-identify data, however, chances of re-identification of attributes always exist as data is collected from multiple sources such as public web, social media, Internet whereabouts, and sensors that are highly prone to data linkages. In literature, k-anonymity stands out amongst the most popular mainstream data anonymization approaches that can also be used for large sized data. However, applying k-anonymization for variety of data (especially unstructured data) is difficult in the traditional way, due to the fact that it requires the given data to be classified into the personal data, the quasi identifiers, and the sensitive data. We identify existing approaches from the literature of Natural Language Processing(NLP) to convert the unstructured data to structured form in order to apply k-anonymization over the generated structured records. We adopt a two phase Conditional Random Field (CRF) based Named Entity Recognition (NER) approach to represent unstructured data into the structured form. Further, we propose an Improved Scalable k-Anonymization (ImSKA) to anonymize the well represented unstructured data that achieves privacy preserving unstructured big data publishing. We compare both of the propose approaches namely NER and ImSKA with existing approaches and the results show that our proposed solutions outperform the existing approaches in terms of F1 score and Normalized Cardinality Penalty (NCP), respectively. Since, NER approaches are widely used for bio-medical datasets, we have also used a well-known Bio-NER dataset called GENIA corpus for measuring the performance. [ABSTRACT FROM AUTHOR]

The Internet protocol (IP) is the lifeblood of the modern Internet. Its simplicity and universality have fueled the unprecedented and lasting global success of the current Internet. Nonetheless, some limitations of IP have been emerging in recent years. Furthermore, starting in mid-1990s, the advent of mobility, wirelessness, and the Web substantially shifted Internet usage and communication paradigms. This accentuated long-term concerns about the current Internet architecture and prompted interest in alternative designs. The U.S. National Science Foundation (NSF) has been one of the key supporters of efforts to design a set of candidate next-generation Internet architectures. As a prominent design requirement, NSF emphasized “security and privacy by design” in order to avoid the long and unhappy history of incremental patching and retrofitting that characterizes the current Internet architecture. To this end, as a result of a competitive process, four prominent research projects were funded by the NSF in 2010: nebula, named-data networking, MobilityFirst, and expressive Internet architecture. This paper provides a comprehensive and neutral analysis of salient security and privacy features (and issues) in these NSF-funded future Internet architectures. Prior surveys on future Internet architectures provide a limited, or even no, comparison on security and privacy features. In addition, this paper also compares the four candidate designs with the current IP-based architecture and discusses similarities, differences, and possible improvements. [ABSTRACT FROM AUTHOR]

Due to the rapid development of wireless communication systems, authentication becomes a key security component in smart grid environments. Authentication then plays an important role in the smart grid domain by providing a variety of security services including credentials’ privacy, session-key (SK) security, and secure mutual authentication. In this paper, we analyze the security of a recent relevant work in smart grid, and it is unfortunately not able to deal with SK-security and smart meter secret credentials’ privacy under the widely accepted Canetti–Krawczyk adversary (CK-adversary) model. We then propose a new efficient provably secure authenticated key agreement scheme for smart grid. Through the rigorous formal security analysis, we show that the proposed scheme achieves the well-known security functionalities including smart meter credentials’ privacy and SK-security under the CK-adversary model. The proposed scheme reduces the computation overheads for both smart meters and service providers. Furthermore, the proposed scheme offers more security functionalities as compared to the existing related schemes. [ABSTRACT FROM PUBLISHER]

The apps installed on a smartphone can reveal much information about a user, such as their medical conditions, sexual orientation, or religious beliefs. In addition, the presence or absence of particular apps on a smartphone can inform an adversary, who is intent on attacking the device. In this paper, we show that a passive eavesdropper can feasibly identify smartphone apps by fingerprinting the network traffic that they send. Although SSL/TLS hides the payload of packets, side-channel data, such as packet size and direction is still leaked from encrypted connections. We use machine learning techniques to identify smartphone apps from this side-channel data. In addition to merely fingerprinting and identifying smartphone apps, we investigate how app fingerprints change over time, across devices, and across different versions of apps. In addition, we introduce strategies that enable our app classification system to identify and mitigate the effect of ambiguous traffic, i.e., traffic in common among apps, such as advertisement traffic. We fully implemented a framework to fingerprint apps and ran a thorough set of experiments to assess its performance. We fingerprinted 110 of the most popular apps in the Google Play Store and were able to identify them six months later with up to 96% accuracy. Additionally, we show that app fingerprints persist to varying extents across devices and app versions. [ABSTRACT FROM PUBLISHER]

Authentication and de-authentication phases should occur at the beginning and end of secure user sessions, respectively. A secure session requires the user to pass the former, but the latter is often underestimated or ignored. Unattended or dangling sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, researchers focused on automated de-authentication systems, either as a stand-alone mechanism or as a result of continuous authentication failures. Unfortunately, no single approach offers security, privacy, and usability. Face-recognition methods, for example, may be suitable for security and usability, but they violate user privacy by continuously recording their actions and surroundings. In this work, we propose BLUFADER, a novel continuous authentication system that takes advantage of blurred face detection and recognition to fast, secure, and transparent de-authenticate users, preserving their privacy. We obfuscate a webcam with a physical blur layer and use deep learning algorithms to perform face detection and recognition continuously. To evaluate BLUFADER's practicality, we collected two datasets formed by 30 recruited subjects (users) and thousands of physically blurred celebrity photos. The de-authentication system was trained and evaluated using the former, while the latter was used to appraise the privacy and increase variance at training time. To guarantee the privacy-preserving effectiveness of the selected physical blurring filter, we show that state-of-the-art deblurring models are not able to revert our physical blur. Further, we demonstrate that our approach outperforms state-of-the-art methods in detecting blurred faces, achieving up to 95% accuracy. Moreover, BLUFADER effectively de-authenticates users up to 100% accuracy in under 3 seconds, while satisfying security, privacy, and usability requirements. Last, our continuous authentication face recognition module based on Siamese Neural Network preventively protect users from adversarial attacks, enhancing the overall system security. [ABSTRACT FROM AUTHOR]

Mobile devices can be maliciously exploited to violate the privacy of people. In most attack scenarios, the adversary takes the local or remote control of the mobile device, by leveraging a vulnerability of the system, hence sending back the collected information to some remote web service. In this paper, we consider a different adversary, who does not interact actively with the mobile device, but he is able to eavesdrop the network traffic of the device from the network side (e.g., controlling a Wi-Fi access point). The fact that the network traffic is often encrypted makes the attack even more challenging. In this paper, we investigate to what extent such an external attacker can identify the specific actions that a user is performing on her mobile apps. We design a system that achieves this goal using advanced machine learning techniques. We built a complete implementation of this system, and we also run a thorough set of experiments, which show that our attack can achieve accuracy and precision higher than 95%, for most of the considered actions. We compared our solution with the three state-of-the-art algorithms, and confirming that our system outperforms all these direct competitors. [ABSTRACT FROM PUBLISHER]

A new privacy model for Location-Based Services (LBSs) has been recently proposed based on users' footprints-these being a repre-sentation of the amount of time a user spends in a given area. Unfortunately, while the model is claimed to be independent from the specific knowledge of the adversary about users' footprints, we argue that an adversary, that has a more structured knowledge over time, can pose a threat to the privacy guarantees of the model. The major contribution of this paper is to show that time is a relevant dimension that needs to be taken into consideration when investigating LBSs privacy issues. In particular, we show that applying our considerations, user privacy can be violated. We support our claim with analysis and a concrete example. Furthermore, by analyzing a real data set of vehicular traces, we show that the threat is actually present in a real scenario and that its effect on jeopardizing user privacy is relevant. [ABSTRACT FROM AUTHOR]