Biblio

Abundant multimedia data generated in our daily life has intrigued a variety of very important and useful real-world applications such as object detection and recognition etc. Accompany with these applications, many popular feature descriptors have been developed, e.g., SIFT, SURF and HOG. Manipulating massive multimedia data locally, however, is a storage and computation intensive task, especially for resource-constrained clients. In this work, we focus on exploring how to securely outsource the famous feature extraction algorithm–Histogram of Oriented Gradients (HOG) to untrusted cloud servers, without revealing the data owner's private information. For the first time, we investigate this secure outsourcing computation problem under two different models and accordingly propose two novel privacy-preserving HOG outsourcing protocols, by efficiently encrypting image data by somewhat homomorphic encryption (SHE) integrated with single-instruction multiple-data (SIMD), designing a new batched secure comparison protocol, and carefully redesigning every step of HOG to adapt it to the ciphertext domain. Explicit Security and effectiveness analysis are presented to show that our protocols are practically-secure and can approximate well the performance of the original HOG executed in the plaintext domain. Our extensive experimental evaluations further demonstrate that our solutions achieve high efficiency and perform comparably to the original HOG when being applied to human detection.

Large scale biomedical research projects involve analysis of huge amount of genomic data which is owned by different data owners. The collection and storing of genomic data is sometimes beyond the capability of a sole organization. Genomic data sharing is a feasible solution to overcome this problem. These scenarios can be generalized into the problem of aggregating data distributed among multiple databases and owned by different data owners. However, we should guarantee that an adversary cannot learn anything about the data or the individual contribution of each party towards the final output of the computation. In this paper, we propose a practical solution for secure sharing and computation of genomic data. We adopt the Paillier cryptosystem and the order preserving encryption to securely execute the count query and the ranked query. Experimental results demonstrate that the computation time is realistic enough to make our system adoptable in the real world.

Funded under the European Union's Horizon 2020 research and innovation programme, SAFEcrypto will provide a new generation of practical, robust and physically secure post-quantum cryptographic solutions that ensure long-term security for future ICT systems, services and applications. The project will focus on the remarkably versatile field of Lattice-based cryptography as the source of computational hardness, and will deliver optimised public key security primitives for digital signatures and authentication, as well identity based encryption (IBE) and attribute based encryption (ABE). This will involve algorithmic and design optimisations, and implementations of lattice-based cryptographic schemes addressing cost, energy consumption, performance and physical robustness. As the National Institute of Standards and Technology (NIST) prepares for the transition to a post-quantum cryptographic suite B, urging organisations that build systems and infrastructures that require long-term security to consider this transition in architectural designs; the SAFEcrypto project will provide Proof-of-concept demonstrators of schemes for three practical real-world case studies with long-term security requirements, in the application areas of satellite communications, network security and cloud. The goal is to affirm Lattice-based cryptography as an effective replacement for traditional number-theoretic public-key cryptography, by demonstrating that it can address the needs of resource-constrained embedded applications, such as mobile and battery-operated devices, and of real-time high performance applications for cloud and network management infrastructures.

We present a unified framework for studying secure multiparty computation (MPC) with arbitrarily restricted interaction patterns such as a chain, a star, a directed tree, or a directed graph. Our study generalizes both standard MPC and recent models for MPC with specific restricted interaction patterns, such as those studied by Halevi et al. (Crypto 2011), Goldwasser et al. (Eurocrypt 2014), and Beimel et al. (Crypto 2014). Since restricted interaction patterns cannot always yield full security for MPC, we start by formalizing the notion of "best possible security" for any interaction pattern. We then obtain the following main results: Completeness theorem. We prove that the star interaction pattern is complete for the problem of MPC with general interaction patterns. Positive results. We present both information-theoretic and computationally secure protocols for computing arbitrary functions with general interaction patterns. We also present more efficient protocols for computing symmetric functions, both in the computational and in the information-theoretic setting. Our computationally secure protocols for general functions necessarily rely on indistinguishability obfuscation while the ones for computing symmetric functions make simple use of multilinear maps. Negative results. We show that, in many cases, the complexity of our information-theoretic protocols is essentially the best that can be achieved. All of our protocols rely on a correlated randomness setup, which is necessary in our setting (for computing general functions). In the computational case, we also present a generic procedure to make any correlated randomness setup reusable, in the common random string model. Although most of our information-theoretic protocols have exponential complexity, they may be practical for functions on small domains (e.g., f0; 1g20), where they are concretely faster than their computational counterparts.

We propose secure RAID, i.e., low-complexity schemes to store information in a distributed manner that is resilient to node failures and resistant to node eavesdropping. We generalize the concept of systematic encoding to secure RAID and show that systematic schemes have significant advantages in the efficiencies of encoding, decoding and random access. For the practical high rate regime, we construct three XOR-based systematic secure RAID schemes with optimal encoding and decoding complexities, from the EVENODD codes and B codes, which are array codes widely used in the RAID architecture. These schemes optimally tolerate two node failures and two eavesdropping nodes. For more general parameters, we construct efficient systematic secure RAID schemes from Reed-Solomon codes. Our results suggest that building “keyless”, information-theoretic security into the RAID architecture is practical.

In this paper we present Securebox, an affordable and deployable platform for securing and managing IoT networks. Our proposal targets an alarming spot in the fast growing IoT industry where security is often overlooked due to device limitation, budget constraint, and development deadline. In contrast to existing host-centric and hardware-coupled solutions, Securebox empowers a cloud-assisted "charge for network service" model that is dedicated to budget and resource constrained IoT environments. Owing to its cloud-driven and modular design, Securebox allows us to 1) flexibly offload and onload security and management functions to the cloud and network edge components; 2) offer advanced security and management services to end users in an affordable and on-demand manner; 3) ease the upgrade and deployment of new services to guard against abrupt security breakouts. To demonstrate Securebox, we have implemented the platform consisting of a plug-n-play frontend, a Kubernetes-powered backend cluster, and a smartphone mobile application. Based on the testbed evaluation, we show that Securebox is robust and responsive. Its collaborative and extensible architecture enforces rapid update cycles and can scale with the growing diversity of IoT devices.

Wireless Sensor Network (WSN) consists of a numerous of small devices called sensor which has a limitation in resources such as low energy, memory, and computation. Sensors deployed in a harsh environment and vulnerable to various security issues and due to the resource restriction in a sensor, key management and provide robust security in this type of networks is a challenge. keys may be used in two ways in cryptography is symmetric or asymmetric, asymmetric is required more communication, memory, and computing when compared with symmetric, so it is not appropriate for WSN. In this paper, key management scheme based on symmetric keys has been proposed where each node uses pseudo-random generator (PRNG)to generate key that is shared with base station based on pre-distributed initial key and CBC - RC5 to reached to confidently, integrity and authentication.

The market of wearable healthcare monitoring devices has exploded in recent years as healthcare consciousness has increased. These types of devices usually consist of several biosensors, which can be worn on human bodies, such as the head, arms, and feet. The health status of a user can be analyzed according to the user's real-time vital signs that are collected from different biosensors. Due to personal medical data being transmitted through a wireless network, the data have to be encrypted. In this paper, a key agreement protocol for biosensors within Wireless Body Sensor Networks (WBSN) has been proposed based on the n-Party Diffie-Hellman key exchange protocol. In order to prevent the man-in-the-middle attacks, we have used Advance Encryption Standard (AES) encryption with Electrocardiography-based (ECG-based) keys to secure the key exchange procedures. The security and performance analysis show the feasibility of the proposed method.

Cloud computing is a wide-spread technology that enables the enterprises to provide services to their customers with a lower cost, higher performance, better availability and scalability. However, privacy and security in cloud computing has always been a major challenge to service providers and a concern to its users. Trusted computing has led its way in securing the cloud computing and virtualized environment, during the past decades. In this paper, first we study virtualized trusted platform modules and integration of vTPM in hypervisor-based virtualization. Then we propose two architectural solutions for integrating the vTPM in container-based virtualization model.

Sego is a hypervisor-based system that gives strong privacy and integrity guarantees to trusted applications, even when the guest operating system is compromised or hostile. Sego verifies operating system services, like the file system, instead of replacing them. By associating trusted metadata with user data across all system devices, Sego verifies system services more efficiently than previous systems, especially services that depend on data contents. We extensively evaluate Sego's performance on real workloads and implement a kernel fault injector to validate Sego's file system-agnostic crash consistency and recovery protocol.

Sego is a hypervisor-based system that gives strong privacy and integrity guarantees to trusted applications, even when the guest operating system is compromised or hostile. Sego verifies operating system services, like the file system, instead of replacing them. By associating trusted metadata with user data across all system devices, Sego verifies system services more efficiently than previous systems, especially services that depend on data contents. We extensively evaluate Sego's performance on real workloads and implement a kernel fault injector to validate Sego's file system-agnostic crash consistency and recovery protocol.

Smart Spaces are composed of heterogeneous sensors and devices that collect and share information. This information may contain personal information of the users. Thus, securing the data and preserving the privacy are of paramount importance. In this paper, we propose techniques for information security and privacy protection for Smart Spaces based on the Smart-M3 platform. We propose a) a security framework, and b) a context-aware role-based access control scheme. We model our access control scheme using ontological techniques and Web Ontology Language (OWL), and implement it via CLIPS rules. To evaluate the efficiency of our access control scheme, we measure the time it takes to check the access rights of the access requests. The results demonstrate that the highest response time is approximately 0.2 seconds in a set of 100000 triples. We conclude that the proposed access control scheme produces low overhead and is therefore, an efficient approach for Smart Spaces.

The average computer user is no longer restricted to one device. They may have several devices and expect their applications to work on all of them. A challenge arises when these applications need the cryptographic private key of the devices' owner. Here the device owner typically has to manage keys manually with a "keychain" app, which leads to private keys being transferred insecurely between devices – or even to other people. Even with intuitive synchronization mechanisms, theft and malware still pose a major risk to keys. Phones and watches are frequently removed or set down, and a single compromised device leads to the loss of the owner's private key, a catastrophic failure that can be quite difficult to recover from. We introduce Shatter, an open-source framework that runs on desktops, Android, and Android Wear, and performs key distribution on a user's behalf. Shatter uses threshold cryptography to turn the security weakness of having multiple devices into a strength. Apps that delegate cryptographic operations to Shatter have their keys compromised only when a threshold number of devices are compromised by the same attacker. We demonstrate how our framework operates with two popular Android apps (protecting identity keys for a messaging app, and encryption keys for a note-taking app) in a backwards-compatible manner: only Shatter users need to move to a Shatter-aware version of the app. Shatter has minimal impact on app performance, with signatures and decryption being calculated in 0.5s and security proofs in 14s.

Video surveillance has been widely adopted to ensure home security in recent years. Most video encoding standards such as H.264 and MPEG-4 compress the temporal redundancy in a video stream using difference coding, which only encodes the residual image between a frame and its reference frame. Difference coding can efficiently compress a video stream, but it causes side-channel information leakage even though the video stream is encrypted, as reported in this paper. Particularly, we observe that the traffic patterns of an encrypted video stream are different when a user conducts different basic activities of daily living, which must be kept private from third parties as obliged by HIPAA regulations. We also observe that by exploiting this side-channel information leakage, attackers can readily infer a user's basic activities of daily living based on only the traffic size data of an encrypted video stream. We validate such an attack using two off-the-shelf cameras, and the results indicate that the user's basic activities of daily living can be recognized with a high accuracy.

This paper proposes a novel measure for edge significance considering quantity propagation in a graph. Our method utilizes a pseudo propagation process brought by solving a problem of a load balancing on nodes. Edge significance is defined as a difference of propagation in a graph with an edge to without it. The simulation compares our proposed method with the traditional betweenness centrality in order to obtain differences of our measure to a type of centrality, which considers propagation process in a graph.

As non-volatile memory (NVM) technologies are expected to replace DRAM in the near future, new challenges have emerged. For example, NVMs have slow and power-consuming writes, and limited write endurance. In addition, NVMs have a data remanence vulnerability, i.e., they retain data for a long time after being powered off. NVM encryption alleviates the vulnerability, but exacerbates the limited endurance by increasing the number of writes to memory. We observe that, in current systems, a large percentage of main memory writes result from data shredding in operating systems, a process of zeroing out physical pages before mapping them to new processes, in order to protect previous processes' data. In this paper, we propose Silent Shredder, which repurposes initialization vectors used in standard counter mode encryption to completely eliminate the data shredding writes. Silent Shredder also speeds up reading shredded cache lines, and hence reduces power consumption and improves overall performance. To evaluate our design, we run three PowerGraph applications and 26 multi-programmed workloads from the SPEC 2006 suite, on a gem5-based full system simulator. Silent Shredder eliminates an average of 48.6% of the writes in the initialization and graph construction phases. It speeds up main memory reads by 3.3 times, and improves the number of instructions per cycle (IPC) by 6.4% on average. Finally, we discuss several use cases, including virtual machines' data isolation and user-level large data initialization, where Silent Shredder can be used effectively at no extra cost.

Internet censorship is used in many parts of the world to prohibit free access to online information. Different techniques such as IP address or URL blocking, DNS hijacking, or deep packet inspection are used to block access to specific content on the Internet. In response, several censorship circumvention systems were proposed that attempt to bypass existing filters. Especially systems that hide the communication in different types of cover protocols attracted a lot of attention. However, recent research results suggest that this kind of covert traffic can be easily detected by censors. In this paper, we present SkypeLine, a censorship circumvention system that leverages Direct-Sequence Spread Spectrum (DSSS) based steganography to hide information in Voice-over-IP (VoIP) communication. SkypeLine introduces two novel modulation techniques that hide data by modulating information bits on the voice carrier signal using pseudo-random, orthogonal noise sequences and repeating the spreading operation several times. Our design goals focus on undetectability in presence of a strong adversary and improved data rates. As a result, the hiding is inconspicuous, does not alter the statistical characteristics of the carrier signal, and is robust against alterations of the transmitted packets. We demonstrate the performance of SkypeLine based on two simulation studies that cover the theoretical performance and robustness. Our measurements demonstrate that the data rates achieved with our techniques substantially exceed existing DSSS approaches. Furthermore, we prove the real-world applicability of the presented system with an exemplary prototype for Skype.

We examine the security of home smart locks: cyber-physical devices that replace traditional door locks with deadbolts that can be electronically controlled by mobile devices or the lock manufacturer's remote servers. We present two categories of attacks against smart locks and analyze the security of five commercially-available locks with respect to these attacks. Our security analysis reveals that flaws in the design, implementation, and interaction models of existing locks can be exploited by several classes of adversaries, allowing them to learn private information about users and gain unauthorized home access. To guide future development of smart locks and similar Internet of Things devices, we propose several defenses that mitigate the attacks we present. One of these defenses is a novel approach to securely and usably communicate a user's intended actions to smart locks, which we prototype and evaluate. Ultimately, our work takes a first step towards illuminating security challenges in the system design and novel functionality introduced by emerging IoT systems.

Wearable devices, such as smartwatches, are furnished with state-of-the-art sensors that enable a range of context-aware applications. However, malicious applications can misuse these sensors, if access is left unaudited. In this paper, we demonstrate how applications that have access to motion or inertial sensor data on a modern smartwatch can recover text typed on an external QWERTY keyboard. Due to the distinct nature of the perceptible motion sensor data, earlier research efforts on emanation based keystroke inference attacks are not readily applicable in this scenario. The proposed novel attack framework characterizes wrist movements (captured by the inertial sensors of the smartwatch worn on the wrist) observed during typing, based on the relative physical position of keys and the direction of transition between pairs of keys. Eavesdropped keystroke characteristics are then matched to candidate words in a dictionary. Multiple evaluations show that our keystroke inference framework has an alarmingly high classification accuracy and word recovery rate. With the information recovered from the wrist movements perceptible by a smartwatch, we exemplify the risks associated with unaudited access to seemingly innocuous sensors (e.g., accelerometers and gyroscopes) of wearable devices. As part of our efforts towards preventing such side-channel attacks, we also develop and evaluate a novel context-aware protection framework which can be used to automatically disable (or downgrade) access to motion sensors, whenever typing activity is detected.

We introduce a scalable observer architecture to estimate the states of a discrete-time linear-time-invariant (LTI) system whose sensors can be manipulated by an attacker. Given the maximum number of attacked sensors, we build on previous results on necessary and sufficient conditions for state estimation, and propose a novel multi-modal Luenberger (MML) observer based on efficient Satisfiability Modulo Theory (SMT) solving. We present two techniques to reduce the complexity of the estimation problem. As a first strategy, instead of a bank of distinct observers, we use a family of filters sharing a single dynamical equation for the states, but different output equations, to generate estimates corresponding to different subsets of sensors. Such an architecture can reduce the memory usage of the observer from an exponential to a linear function of the number of sensors. We then develop an efficient SMT-based decision procedure that is able to reason about the estimates of the MML observer to detect at runtime which sets of sensors are attack-free, and use them to obtain a correct state estimate. We provide proofs of convergence for our algorithm and report simulation results to compare its runtime performance with alternative techniques. Our algorithm scales well for large systems (including up to 5000 sensors) for which many previously proposed algorithms are not implementable due to excessive memory and time requirements. Finally, we illustrate the effectiveness of our algorithm on the design of resilient power distribution systems.

The exploitation of the opportunistic infrastructure via Device-to-Device (D2D) communication is a critical component towards the adoption of new paradigms such as edge and fog computing. While a lot of work has demonstrated the great potential of D2D communication, it is still unclear whether the benefits of the D2D approach can really be leveraged in practice. In this paper, we develop a software sensor, namely Detector, which senses the infrastructure in proximity of a mobile user. We analyze and evaluate D2D on the wild, i.e., not in simulations. We found that in a realistic environment, a mobile is always co-located in proximity to at least one other mobile device throughout the day. This suggests that a device can schedule tasks processing in coordination with other devices, potentially more powerful, instead of handling the processing of the tasks by itself.

Virtual machine live migration technology, as an important support for cloud computing, has become a central issue in recent years. The virtual machines' runtime environment is migrated from the original physical server to another physical server, maintaining the virtual machines running at the same time. Therefore, it can make load balancing among servers and ensure the quality of service. However, virtual machine migration security issue cannot be ignored due to the immature development of it. This paper we analyze the security threats of the virtual machine migration, and compare the current proposed protection measures. While, these methods either rely on hardware, or lack adequate security and expansibility. In the end, we propose a security model of live virtual machine migration based on security policy transfer and encryption, named as SPLM (Security Protection of Live Migration) and analyze its security and reliability, which proves that SPLM is better than others. This paper can be useful for the researchers to work on this field. The security study of live virtual machine migration in this paper provides a certain reference for the research of virtualization security, and is of great significance.