Biblio

Behavior-based tracking is an unobtrusive technique that allows observers to monitor user activities on the Internet over long periods of time – in spite of changing IP addresses. Previous work has employed supervised classifiers in order to link the sessions of individual users. However, classifiers need labeled training sessions, which are difficult to obtain for observers. In this paper we show how this limitation can be overcome with an unsupervised learning technique. We present a modified k-means algorithm and evaluate it on a realistic dataset that contains the Domain Name System (DNS) queries of 3,862 users. For this purpose, we simulate an observer that tries to track all users, and an Internet Service Provider that assigns a different IP address to every user on every day. The highest tracking accuracy is achieved within the subgroup of highly active users. Almost all sessions of 73% of the users in this subgroup can be linked over a period of 56 days. 19% of the highly active users can be traced completely, i.e., all their sessions are assigned to a single cluster. This fraction increases to 40% for shorter periods of seven days. As service providers may engage in behavior-based tracking to complement their existing profiling efforts, it constitutes a severe privacy threat for users of online services. Users can defend against behavior-based tracking by changing their IP address frequently, but this is cumbersome at the moment.

Ethernet technology dominates enterprise and home network installations and is present in datacenters as well as parts of the backbone of the Internet. Due to its wireline nature, Ethernet networks are often assumed to intrinsically protect the exchanged data against attacks carried out by eavesdroppers and malicious attackers that do not have physical access to network devices, patch panels and network outlets. In this work, we practically evaluate the possibility of wireless attacks against wired Ethernet installations with respect to resistance against eavesdropping by using off-the-shelf software-defined radio platforms. Our results clearly indicate that twisted-pair network cables radiate enough electromagnetic waves to reconstruct transmitted frames with negligible bit error rates, even when the cables are not damaged at all. Since this allows an attacker to stay undetected, it urges the need for link layer encryption or physical layer security to protect confidentiality.

With the emergence of the internet of things (IoT) and participatory sensing (PS) paradigms trustworthiness of remotely sensed data has become a vital research question. In this work, we present the design of a trusted sensor, which uses physically unclonable functions (PUFs) as anchor to ensure integrity, authenticity and non-repudiation guarantees on the sensed data. We propose trusted sensors for mobile devices to address the problem of potential manipulation of mobile sensors' readings by exploiting vulnerabilities of mobile device OS in participatory sensing for IoT applications. Preliminary results from our implementation of trusted visual sensor node show that the proposed security solution can be realized without consuming significant amount of resources of the sensor node.

There has been a tremendous increase in popularity and adoption of wearable fitness trackers. These fitness trackers predominantly use Bluetooth Low Energy (BLE) for communicating and syncing the data with user's smartphone. This paper presents a measurement-driven study of possible privacy leakage from BLE communication between the fitness tracker and the smartphone. Using real BLE traffic traces collected in the wild and in controlled experiments, we show that majority of the fitness trackers use unchanged BLE address while advertising, making it feasible to track them. The BLE traffic of the fitness trackers is found to be correlated with the intensity of user's activity, making it possible for an eavesdropper to determine user's current activity (walking, sitting, idle or running) through BLE traffic analysis. Furthermore, we also demonstrate that the BLE traffic can represent user's gait which is known to be distinct from user to user. This makes it possible to identify a person (from a small group of users) based on the BLE traffic of her fitness tracker. As BLE-based wearable fitness trackers become widely adopted, our aim is to identify important privacy implications of their usage and discuss prevention strategies.

UnlimitID is a method for enhancing the privacy of commodity OAuth and applications such as OpenID Connect, using anonymous attribute-based credentials based on algebraic Message Authentication Codes (aMACs). OAuth is one of the most widely used protocols on the Web, but it exposes each of the requests of a user for data by each relying party (RP) to the identity provider (IdP). Our approach allows for the creation of multiple persistent and unlinkable pseudo-identities and requires no change in the deployed code of relying parties, only in identity providers and the client.

In a wireless system, a signal map shows the signal strength at different locations termed reference points (RPs). As access points (APs) and their transmission power may change over time, keeping an updated signal map is important for applications such as Wi-Fi optimization and indoor localization. Traditionally, the signal map is obtained by a full site survey, which is time-consuming and costly. We address in this paper how to efficiently update a signal map given sparse samples randomly crowdsourced in the space (e.g., by signal monitors, explicit human input, or implicit user participation). We propose Compressive Signal Reconstruction (CSR), a novel learning system employing Bayesian compressive sensing (BCS) for online signal map update. CSR does not rely on any path loss model or line of sight, and is generic enough to serve as a plug-in of any wireless system. Besides signal map update, CSR also computes the estimation error of signals in terms of confidence interval. CSR models the signal correlation with a kernel function. Using it, CSR constructs a sensing matrix based on the newly sampled signals. The sensing matrix is then used to compute the signal change at all the RPs with any BCS algorithm. We have conducted extensive experiments on CSR in our university campus. Our results show that CSR outperforms other state-of-the-art algorithms by a wide margin (reducing signal error by about 30% and sampling points by 20%).

This paper presents a framework for privacy-preserving video delivery system to fulfill users' privacy demands. The proposed framework leverages the inference channels in sensitive behavior prediction and object tracking in a video surveillance system for the sequence privacy protection. For such a goal, we need to capture different pieces of evidence which are used to infer the identity. The temporal, spatial and context features are extracted from the surveillance video as the observations to perceive the privacy demands and their correlations. Taking advantage of quantifying various evidence and utility, we let users subscribe videos with a viewer-dependent pattern. We implement a prototype system for off-line and on-line requirements in two typical monitoring scenarios to construct extensive experiments. The evaluation results show that our system can efficiently satisfy users' privacy demands while saving over 25% more video information compared to traditional video privacy protection schemes.

We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy-preserving version of her data. In this paper, the value of ε units of privacy is measured by the minimum payment of all nonnegative payment mechanisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ε. The higher ε is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects becomes large. Specifically, the lower bound assures that it is impossible to use less amount of payment to buy ε units of privacy, and the upper bound is given by an achievable payment mechanism that we designed. Based on these fundamental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individual's payment away from the minimum.

In this study, we present WindTalker, a novel and practical keystroke inference framework that allows an attacker to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from the observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). The adversary can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's number input. WindTalker presents a novel approach to collect the target's CSI data by deploying a public WiFi hotspot. Compared with the previous keystroke inference approach, WindTalker neither deploys external devices close to the target device nor compromises the target device. Instead, it utilizes the public WiFi to collect user's CSI data, which is easy-to-deploy and difficult-to-detect. In addition, it jointly analyzes the traffic and the CSI to launch the keystroke inference only for the sensitive period where password entering occurs. WindTalker can be launched without the requirement of visually seeing the smart phone user's input process, backside motion, or installing any malware on the tablet. We implemented Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. The evaluation results show that the attacker can recover the key with a high successful rate.

When filling out privacy-related forms in public places such as hospitals or clinics, people usually are not aware that the sound of their handwriting leaks personal information. In this paper, we explore the possibility of eavesdropping on handwriting via nearby mobile devices based on audio signal processing and machine learning. By presenting a proof-of-concept system, WritingHacker, we show the usage of mobile devices to collect the sound of victims' handwriting, and to extract handwriting-specific features for machine learning based analysis. WritingHacker focuses on the situation where the victim's handwriting follows certain print style. An attacker can keep a mobile device, such as a common smart-phone, touching the desk used by the victim to record the audio signals of handwriting. Then the system can provide a word-level estimate for the content of the handwriting. To reduce the impacts of various writing habits and writing locations, the system utilizes the methods of letter clustering and dictionary filtering. Our prototype system's experimental results show that the accuracy of word recognition reaches around 50% - 60% under certain conditions, which reveals the danger of privacy leakage through the sound of handwriting.

Ambiguity arises in requirements when astatement is unintentionally or otherwise incomplete, missing information, or when a word or phrase has morethan one possible meaning. For web-based and mobileinformation systems, ambiguity, and vagueness inparticular, undermines the ability of organizations to aligntheir privacy policies with their data practices, which canconfuse or mislead users thus leading to an increase inprivacy risk. In this paper, we introduce a theory ofvagueness for privacy policy statements based on ataxonomy of vague terms derived from an empiricalcontent analysis of 15 privacy policies. The taxonomy wasevaluated in a paired comparison experiment and resultswere analyzed using the Bradley-Terry model to yield arank order of vague terms in both isolation andcomposition. The theory predicts how vague modifiers toinformation actions and information types can becomposed to increase or decrease overall vagueness. Wefurther provide empirical evidence based on factorialvignette surveys to show how increases in vagueness willdecrease users' acceptance of privacy risk and thusdecrease users' willingness to share personal information.

Privacy and security have been discussed in many occasions and in most cases, the importance that these two aspects play on the information system domain are mentioned often. Many times, research is carried out on the individual information security or privacy measures where it is commonly regarded with the focus on the particular measure or both privacy and security are regarded as a whole subject. However, there have been no attempts at establishing a proper method in categorizing any form of objects of protection. Through the review done on this paper, we would like to investigate the relationship between privacy and security and form a break down the aspects of privacy and security in order to provide better understanding through determining if a measure or methodology is security, privacy oriented or both. We would recommend that in further research, a further refined formulation should be formed in order to carry out this determination process. As a result, we propose a Privacy-Security Tree (PST) in this paper that distinguishes the privacy from security measures.

A smart city uses information technology to integrate and manage physical, social, and business infrastructures in order to provide better services to its dwellers while ensuring efficient and optimal utilization of available resources. With the proliferation of technologies such as Internet of Things (IoT), cloud computing, and interconnected networks, smart cities can deliver innovative solutions and more direct interaction and collaboration between citizens and the local government. Despite a number of potential benefits, digital disruption poses many challenges related to information security and privacy. This paper proposes a security framework that integrates the blockchain technology with smart devices to provide a secure communication platform in a smart city.

The current trend of ubiquitous device use whereby computing is becoming increasingly context-aware and personal, has created a growing concern for the protection of personal privacy. Privacy is an essential component of security, and there is a need to be able to secure personal computers and networks to minimize privacy depreciation within cyberspace. Human error has been recognized as playing a major role in security breaches: Hence technological solutions alone cannot adequately address the emerging security and privacy threats. Home users are particularly vulnerable to cybersecurity threats for a number of reasons, including a particularly important one that our research seeks to address: The lack of cybersecurity education. We argue that research seeking to address the human element of cybersecurity should not be limited only to the design of more usable technical security mechanisms, but should be extended and applied to offering appropriate training to all stakeholders within cyberspace.

In this work, we introduce a new Markov operator associated with a digraph, which we refer to as a nonlinear Laplacian. Unlike previous Laplacians for digraphs, the nonlinear Laplacian does not rely on the stationary distribution of the random walk process and is well defined on digraphs that are not strongly connected. We show that the nonlinear Laplacian has nontrivial eigenvalues and give a Cheeger-like inequality, which relates the conductance of a digraph and the smallest non-zero eigenvalue of its nonlinear Laplacian. Finally, we apply the nonlinear Laplacian to the analysis of real-world networks and obtain encouraging results.

We initiate the study of a quantity that we call coordination complexity. In a distributed optimization problem, the information defining a problem instance is distributed among n parties, who need to each choose an action, which jointly will form a solution to the optimization problem. The coordination complexity represents the minimal amount of information that a centralized coordinator, who has full knowledge of the problem instance, needs to broadcast in order to coordinate the n parties to play a nearly optimal solution. We show that upper bounds on the coordination complexity of a problem imply the existence of good jointly differentially private algorithms for solving that problem, which in turn are known to upper bound the price of anarchy in certain games with dynamically changing populations. We show several results. We fully characterize the coordination complexity for the problem of computing a many-to-one matching in a bipartite graph. Our upper bound in fact extends much more generally to the problem of solving a linearly separable convex program. We also give a different upper bound technique, which we use to bound the coordination complexity of coordinating a Nash equilibrium in a routing game, and of computing a stable matching.

We present the first formal verification of state machine safety for the Raft consensus protocol, a critical component of many distributed systems. We connected our proof to previous work to establish an end-to-end guarantee that our implementation provides linearizable state machine replication. This proof required iteratively discovering and proving 90 system invariants. Our verified implementation is extracted to OCaml and runs on real networks. The primary challenge we faced during the verification process was proof maintenance, since proving one invariant often required strengthening and updating other parts of our proof. To address this challenge, we propose a methodology of planning for change during verification. Our methodology adapts classical information hiding techniques to the context of proof assistants, factors out common invariant-strengthening patterns into custom induction principles, proves higher-order lemmas that show any property proved about a particular component implies analogous properties about related components, and makes proofs robust to change using structural tactics. We also discuss how our methodology may be applied to systems verification more broadly.

In infrastructure wireless network technology, communication between users is provided within a certain area supported by access points (APs) or base station communication networks, but in ad-hoc networks, communication between users is provided only through direct connections between nodes. Ad-hoc network technology supports mobility directly through routing algorithms. However, when a connected node is lost owing to the node's movement, the routing protocol transfers this traffic to another node. The routing table in the node that is receiving the traffic detects any changes that occur and manages them. This paper proposes a routing protocol method that sets up multi-hops in the ad-hoc network and verifies the performance, which provides more effective connection persistence than existing methods.

Standardization and harmonization efforts have reached a consensus towards using a special-purpose Vehicular Public-Key Infrastructure (VPKI) in upcoming Vehicular Communication (VC) systems. However, there are still several technical challenges with no conclusive answers; one such an important yet open challenge is the acquisition of short-term credentials, pseudonym: how should each vehicle interact with the VPKI, e.g., how frequently and for how long? Should each vehicle itself determine the pseudonym lifetime? Answering these questions is far from trivial. Each choice can affect both the user privacy and the system performance and possibly, as a result, its security. In this paper, we make a novel systematic effort to address this multifaceted question. We craft three generally applicable policies and experimentally evaluate the VPKI system performance, leveraging two large-scale mobility datasets. We consider the most promising, in terms of efficiency, pseudonym acquisition policies; we find that within this class of policies, the most promising policy in terms of privacy protection can be supported with moderate overhead. Moreover, in all cases, this work is the first to provide tangible evidence that the state-of-the-art VPKI can serve sizable areas or domain with modest computing resources.

Bitcoin, a decentralized cryptographic currency that has experienced proliferating popularity over the past few years, is the common denominator in a wide variety of cybercrime. We perform a measurement analysis of CryptoLocker, a family of ransomware that encrypts a victim's files until a ransom is paid, within the Bitcoin ecosystem from September 5, 2013 through January 31, 2014. Using information collected from online fora, such as reddit and BitcoinTalk, as an initial starting point, we generate a cluster of 968 Bitcoin addresses belonging to CryptoLocker. We provide a lower bound for CryptoLocker's economy in Bitcoin and identify 795 ransom payments totalling 1,128.40 BTC (\$310,472.38), but show that the proceeds could have been worth upwards of \$1.1 million at peak valuation. By analyzing ransom payment timestamps both longitudinally across CryptoLocker's operating period and transversely across times of day, we detect changes in distributions and form conjectures on CryptoLocker that corroborate information from previous efforts. Additionally, we construct a network topology to detail CryptoLocker's financial infrastructure and obtain auxiliary information on the CryptoLocker operation. Most notably, we find evidence that suggests connections to popular Bitcoin services, such as Bitcoin Fog and BTC-e, and subtle links to other cybercrimes surrounding Bitcoin, such as the Sheep Marketplace scam of 2013. We use our study to underscore the value of measurement analyses and threat intelligence in understanding the erratic cybercrime landscape.

Along with the ever increasing growth in data collection and its mining, there is an increasing fear of compromising individual and population privacy. Several techniques have been proposed in literature to preserve privacy of collected data while storing and processing. In this paper, we propose a privacy-aware architecture for storing and processing data in a Big Data warehouse. In particular, we propose a flexible, extendable, and adaptable architecture that enforces user specified privacy requirements in the form of Embedded Privacy Agreements. The paper discusses the details of the architecture with some implementation details.

Parallel and distributed systems rely on intricate protocols to manage shared resources and synchronize, i.e., to manage how many processes are in a particular state. Effective verification of such systems requires universally quantification to reason about parameterized state and cardinalities tracking sets of processes, messages, failures to adequately capture protocol logic. In this paper we present Tool, an automatic invariant synthesis method that integrates cardinality-based reasoning and universal quantification. The resulting increase of expressiveness allows Tool to verify, for the first time, a representative collection of intricate parameterized protocols.

Differential privacy has become the dominant standard in the research community for strong privacy protection. There has been a flood of research into query answering algorithms that meet this standard. Algorithms are becoming increasingly complex, and in particular, the performance of many emerging algorithms is data dependent, meaning the distribution of the noise added to query answers may change depending on the input data. Theoretical analysis typically only considers the worst case, making empirical study of average case performance increasingly important. In this paper we propose a set of evaluation principles which we argue are essential for sound evaluation. Based on these principles we propose DPBench, a novel evaluation framework for standardized evaluation of privacy algorithms. We then apply our benchmark to evaluate algorithms for answering 1- and 2-dimensional range queries. The result is a thorough empirical study of 15 published algorithms on a total of 27 datasets that offers new insights into algorithm behavior–-in particular the influence of dataset scale and shape–-and a more complete characterization of the state of the art. Our methodology is able to resolve inconsistencies in prior empirical studies and place algorithm performance in context through comparison to simple baselines. Finally, we pose open research questions which we hope will guide future algorithm design.

An increasing number of everyday objects are now connected to the internet, collecting and sharing information about us: the "Internet of Things" (IoT). However, as the number of "social" objects increases, human concerns arising from this connected world are starting to become apparent. This paper presents the results of a preliminary qualitative study in which five participants lived with an ambiguous IoT device that collected and shared data about their activities at home for a week. In analyzing this data, we identify the nature of human and socio-technical concerns that arise when living with IoT technologies. Trust is identified as a critical factor - as trust in the entity/ies that are able to use their collected information decreases, users are likely to demand greater control over information collection. Addressing these concerns may support greater engagement of users with IoT technology. The paper concludes with a discussion of how IoT systems might be designed to better foster trust with their owners.

After a brief introduction on optical chaotic cryptography, we compare the standard short cavity, close-loop, two-laser and three-laser schemes for secure transmission, showing that both are suitable for secure data exchange, the three-laser scheme offering a slightly better level of privacy, due to its symmetrical topology.