Biblio

It is a fundamental problem to decide how many copies of an unknown mixed quantum state are necessary and sufficient to determine the state. This is the quantum analogue of the problem of estimating a probability distribution given some number of samples. Previously, it was known only that estimating states to error є in trace distance required O(dr2/є2) copies for a d-dimensional density matrix of rank r. Here, we give a measurement scheme (POVM) that uses O( (dr/ δ ) ln(d/δ) ) copies to estimate ρ to error δ in infidelity. This implies O( (dr / є2)· ln(d/є) ) copies suffice to achieve error є in trace distance. For fixed d, our measurement can be implemented on a quantum computer in time polynomial in n. We also use the Holevo bound from quantum information theory to prove a lower bound of Ω(dr/є2)/ log(d/rє) copies needed to achieve error є in trace distance. This implies a lower bound Ω(dr/δ)/log(d/rδ) for the estimation error δ in infidelity. These match our upper bounds up to log factors. Our techniques can also show an Ω(r2d/δ) lower bound for measurement strategies in which each copy is measured individually and then the outcomes are classically post-processed to produce an estimate. This matches the known achievability results and proves for the first time that such “product” measurements have asymptotically suboptimal scaling with d and r.

Large numbers of smart connected devices, also named as the Internet of Things (IoT), are permeating our environments (homes, factories, cars, and also our body - with wearable devices) to collect data and act on the insight derived. Ensuring software integrity (including OS, apps, and configurations) on such smart devices is then essential to guarantee both privacy and safety. A key mechanism to protect the software integrity of these devices is remote attestation: A process that allows a remote verifier to validate the integrity of the software of a device. This process usually makes use of a signed hash value of the actual device's software, generated by dedicated hardware. While individual device attestation is a well-established technique, to date integrity verification of a very large number of devices remains an open problem, due to scalability issues. In this paper, we present SANA, the first secure and scalable protocol for efficient attestation of large sets of devices that works under realistic assumptions. SANA relies on a novel signature scheme to allow anyone to publicly verify a collective attestation in constant time and space, for virtually an unlimited number of devices. We substantially improve existing swarm attestation schemes by supporting a realistic trust model where: (1) only the targeted devices are required to implement attestation; (2) compromising any device does not harm others; and (3) all aggregators can be untrusted. We implemented SANA and demonstrated its efficiency on tiny sensor devices. Furthermore, we simulated SANA at large scale, to assess its scalability. Our results show that SANA can provide efficient attestation of networks of 1,000,000 devices, in only 2.5 seconds.

Recent literature on iOS security has focused on the malicious potential of third-party applications, demonstrating how developers can bypass application vetting and code-level protections. In addition to these protections, iOS uses a generic sandbox profile called "container" to confine malicious or exploited third-party applications. In this paper, we present the first systematic analysis of the iOS container sandbox profile. We propose the SandScout framework to extract, decompile, formally model, and analyze iOS sandbox profiles as logic-based programs. We use our Prolog-based queries to evaluate file-based security properties of the container sandbox profile for iOS 9.0.2 and discover seven classes of exploitable vulnerabilities. These attacks affect non-jailbroken devices running later versions of iOS. We are working with Apple to resolve these attacks, and we expect that SandScout will play a significant role in the development of sandbox profiles for future versions of iOS.

Nanonetworks consist of nanomachines that perform simple tasks (sensing, data processing and communication) at molecular scale. Nanonetworks promise novel solutions in various fields, such as biomedical, industrial and military. Reliable nanocommunications require error control. ARQ and complex Forward Error Correction (FEC) are not appropriate in nano-devices due to the peculiarities of Terahertz band, limited computation complexity and energy capacity. In this paper we propose Simple Block Nanocode (SBN) to provide reliable data transmission in electromagnetic nanocommunications. We compare it with the two reliable transmission codes in nanonetworks in the literature, minimum energy channel (MEC) and Low Weight Channel (LWC) codes. The results show that SBN outperforms MEC and LWC in terms of reliability and image quality at receiver. The results also show that a nano-device (with nano-camera) with harvesting module has enough energy to support perpetual image transmission.

Attack graphs are a powerful modeling technique with which to explore the attack surface of a system. However, they can be difficult to generate due to the exponential growth of the state space, often times making exhaustive search impractical. This paper discusses an approach for generating large attack graphs with an emphasis on scalable generation over a distributed system. First, a serial algorithm is presented, highlighting bottlenecks and opportunities to exploit inherent concurrency in the generation process. Then a strategy to parallelize this process is presented. Finally, we discuss plans for future work to implement the parallel algorithm using a hybrid distributed/shared memory programming model on a heterogeneous compute node cluster.

While cloud computing has become an attractive platform for supporting data intensive applications, a major obstacle to the adoption of cloud computing in sectors such as health and defense is the privacy risk associated with releasing datasets to third-parties in the cloud for analysis. A widely-adopted technique for data privacy preservation is to anonymize data via local recoding. However, most existing local-recoding techniques are either serial or distributed without directly optimizing scalability, thus rendering them unsuitable for big data applications. In this paper, we propose a highly scalable approach to local-recoding anonymization in cloud computing, based on Locality Sensitive Hashing (LSH). Specifically, a novel semantic distance metric is presented for use with LSH to measure the similarity between two data records. Then, LSH with the MinHash function family can be employed to divide datasets into multiple partitions for use with MapReduce to parallelize computation while preserving similarity. By using our efficient LSH-based scheme, we can anonymize each partition through the use of a recursive agglomerative \$k\$-member clustering algorithm. Extensive experiments on real-life datasets show that our approach significantly improves the scalability and time-efficiency of local-recoding anonymization by orders of magnitude over existing approaches.

Threat classification is extremely important for individuals and organizations, as it is an important step towards realization of information security. In fact, with the progress of information technologies (IT) security becomes a major challenge for organizations which are vulnerable to many types of insiders and outsiders security threats. The paper deals with threats classification models in order to help managers to define threat characteristics and then protect their assets from them. Existing threats classification models are non complete and present non orthogonal threats classes. The aim of this paper is to suggest a scalable and complete approach that classifies security threat in orthogonal way.

The National Science Foundation has made investments in Software Defined Networking (SDN) and Network Function Virtualization (NFV) for many years, in both the research and infrastructure areas. SDN and NFV enable systems to become more open to transformative research, with implications for revolutionary new applications and services. Additionally, the emerging concept of Software-Defined Exchanges will enable large-scale interconnection of Software Defined infrastructures, owned and operated by many different organizations, to provide logically isolated 'on demand' global scale infrastructure on an end-to-end basis, with enhanced flexibility and security for new applications. This talk will examine past NSF investments and successes in SDN/NFV, identify new research opportunities available to the community and present challenges that need to be overcome to make SDN/NFV a reality in operational cyberinfrastructure.

Software Defined Networking (SDN) is the new promise towards an easily configured and remotely controlled network. Based on Centralized control, SDN technology has proved its positive impact on the world of network communications from different aspects. Security in SDN, as in traditional networks, is an essential feature that every communication system should possess. In this paper, we propose an SDN security design approach, which strikes a good balance between network performance and security features. We show how such an approach can be used to prevent DDoS attacks targeting either the controller or the different hosts in the network, and how to trace back the source of the attack. The solution lies in introducing a third plane, the security plane, in addition to the data plane, which is responsible for forwarding data packets between SDN switches, and parallel to the control plane, which is responsible for rule and data exchange between the switches and the SDN controller. The security plane is designed to exchange security-related data between a third party agent on the switch and a third party software module alongside the controller. Our evaluation shows the capability of the proposed system to enforce different levels of real-time user-defined security with low overhead and minimal configuration.

Searchable symmetric encryption (SSE) enables a client to store a database on an untrusted server while supporting keyword search in a secure manner. Despite the rapidly increasing interest in SSE technology, experiments indicate that the performance of the known schemes scales badly to large databases. Somewhat surprisingly, this is not due to their usage of cryptographic tools, but rather due to their poor locality (where locality is defined as the number of non-contiguous memory locations the server accesses with each query). The only known schemes that do not suffer from poor locality suffer either from an impractical space overhead or from an impractical read efficiency (where read efficiency is defined as the ratio between the number of bits the server reads with each query and the actual size of the answer). We construct the first SSE schemes that simultaneously enjoy optimal locality, optimal space overhead, and nearly-optimal read efficiency. Specifically, for a database of size N, under the modest assumption that no keyword appears in more than N1 − 1/loglogN documents, we construct a scheme with read efficiency Õ(loglogN). This essentially matches the lower bound of Cash and Tessaro (EUROCRYPT ’14) showing that any SSE scheme must be sub-optimal in either its locality, its space overhead, or its read efficiency. In addition, even without making any assumptions on the structure of the database, we construct a scheme with read efficiency Õ(logN). Our schemes are obtained via a two-dimensional generalization of the classic balanced allocations (“balls and bins”) problem that we put forward. We construct nearly-optimal two-dimensional balanced allocation schemes, and then combine their algorithmic structure with subtle cryptographic techniques.

Searchable symmetric encryption (SSE) enables a client to store a database on an untrusted server while supporting keyword search in a secure manner. Despite the rapidly increasing interest in SSE technology, experiments indicate that the performance of the known schemes scales badly to large databases. Somewhat surprisingly, this is not due to their usage of cryptographic tools, but rather due to their poor locality (where locality is defined as the number of non-contiguous memory locations the server accesses with each query). The only known schemes that do not suffer from poor locality suffer either from an impractical space overhead or from an impractical read efficiency (where read efficiency is defined as the ratio between the number of bits the server reads with each query and the actual size of the answer). We construct the first SSE schemes that simultaneously enjoy optimal locality, optimal space overhead, and nearly-optimal read efficiency. Specifically, for a database of size N, under the modest assumption that no keyword appears in more than N1 − 1/loglogN documents, we construct a scheme with read efficiency Õ(loglogN). This essentially matches the lower bound of Cash and Tessaro (EUROCRYPT ’14) showing that any SSE scheme must be sub-optimal in either its locality, its space overhead, or its read efficiency. In addition, even without making any assumptions on the structure of the database, we construct a scheme with read efficiency Õ(logN). Our schemes are obtained via a two-dimensional generalization of the classic balanced allocations (“balls and bins”) problem that we put forward. We construct nearly-optimal two-dimensional balanced allocation schemes, and then combine their algorithmic structure with subtle cryptographic techniques.

Cyber-Physical Systems (CPSs) are often tested at different test levels following "X-in-the-Loop" configurations: Model-, Software- and Hardware-in-the-loop (MiL, SiL and HiL). While MiL and SiL test levels aim at testing functional requirements at the system level, the HiL test level tests functional as well as non-functional requirements by performing a real-time simulation. As testing CPS product line configurations is costly due to the fact that there are many variants to test, test cases are long, the physical layer has to be simulated and co-simulation is often necessary. It is therefore extremely important to select the appropriate test cases that cover the objectives of each level in an allowable amount of time. We propose an efficient test case selection approach adapted to the "X-in-the-Loop" test levels. Search algorithms are employed to reduce the amount of time required to test configurations of CPS product lines while achieving the test objectives of each level. We empirically evaluate three commonly-used search algorithms, i.e., Genetic Algorithm (GA), Alternating Variable Method (AVM) and Greedy (Random Search (RS) is used as a baseline) by employing two case studies with the aim of integrating the best algorithm into our approach. Results suggest that as compared with RS, our approach can reduce the costs of testing CPS product line configurations by approximately 80% while improving the overall test quality.

In today's multicore processors, the last-level cache is often shared by multiple concurrently running processes to make efficient use of hardware resources. However, previous studies have shown that a shared cache is vulnerable to timing channel attacks that leak confidential information from one process to another. Static cache partitioning can eliminate the cache timing channels but incurs significant performance overhead. In this paper, we propose Secure Dynamic Cache Partitioning (SecDCP), a partitioning technique that defeats cache timing channel attacks. The SecDCP scheme changes the size of cache partitions at run time for better performance while preventing insecure information leakage between processes. For cache-sensitive multiprogram workloads, our experimental results show that SecDCP improves performance by up to 43% and by an average of 12.5% over static cache partitioning.

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.

Data persistence in emerging non-volatile memories (NVMs) poses a multitude of security vulnerabilities, motivating main memory encryption for data security. However, practical encryption algorithms demonstrate strong diffusion characteristics that increase cell flips, resulting in increased write energy/latency and reduced lifetime of NVMs. State-of-the-art security solutions have focused on reducing the encryption penalty (increased write energy/latency and reduced memory lifetime) in single-level cell (SLC) NVMs; however, the realization of low encryption penalty solutions for multi-/triple-level cell (MLC/TLC) secure NVMs remains an open area of research. This work synergistically integrates zero-based partial writes with XOR-based energy masking to realize Smartly EnCRypted Energy efficienT, i.e., SECRET MLC/TLC NVMs, without compromising the security of the underlying encryption technique. Our simulations on an MLC (TLC) resistive RAM (RRAM) architecture across SPEC CPU2006 workloads demonstrate that for 6.25% (7.84%) memory overhead, SECRET reduces write energy by 80% (63%), latency by 37% (49%), and improves memory lifetime by 63% (56%) over conventional advanced encryption standard-based (AES-based) counter mode encryption.

Data persistence in emerging non-volatile memories (NVMs) poses a multitude of security vulnerabilities, motivating main memory encryption for data security. However, practical encryption algorithms demonstrate strong diffusion characteristics that increase cell flips, resulting in increased write energy/latency and reduced lifetime of NVMs. State-of-the-art security solutions have focused on reducing the encryption penalty (increased write energy/latency and reduced memory lifetime) in single-level cell (SLC) NVMs; however, the realization of low encryption penalty solutions for multi-/triple-level cell (MLC/TLC) secure NVMs remains an open area of research. This work synergistically integrates zero-based partial writes with XOR-based energy masking to realize Smartly EnCRypted Energy efficienT, i.e., SECRET MLC/TLC NVMs, without compromising the security of the underlying encryption technique. Our simulations on an MLC (TLC) resistive RAM (RRAM) architecture across SPEC CPU2006 workloads demonstrate that for 6.25% (7.84%) memory overhead, SECRET reduces write energy by 80% (63%), latency by 37% (49%), and improves memory lifetime by 63% (56%) over conventional advanced encryption standard-based (AES-based) counter mode encryption.

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.

Cloud computing platforms are becoming increasingly prevalent and readily available nowadays, providing us alternative and economic services for resource-constrained clients to perform large-scale computation. In this work, we address the problem of secure outsourcing of large-scale nonnegative matrix factorization (NMF) to a cloud in a way that the client can verify the correctness of results with small overhead. The input matrix protection is achieved by a lightweight, permutation-based encryption mechanism. By exploiting the iterative nature of NMF computation, we propose a single-round verification strategy, which can be proved to be effective. Both theoretical and experimental results are given to demonstrate the superior performance of our scheme.

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.

Content-based routing (CBR) is a powerful model that supports scalable asynchronous communication among large sets of geographically distributed nodes. Yet, preserving privacy represents a major limitation for the wide adoption of CBR, notably when the routers are located in public clouds. Indeed, a CBR router must see the content of the messages sent by data producers, as well as the filters (or subscriptions) registered by data consumers. This represents a major deterrent for companies for which data is a key asset, as for instance in the case of financial markets or to conduct sensitive business-to-business transactions. While there exists some techniques for privacy-preserving computation, they are either prohibitively slow or too limited to be usable in real systems. In this paper, we follow a different strategy by taking advantage of trusted hardware extensions that have just been introduced in off-the-shelf processors and provide a trusted execution environment. We exploit Intel's new software guard extensions (SGX) to implement a CBR engine in a secure enclave. Thanks to the hardware-based trusted execution environment (TEE), the compute-intensive CBR operations can operate on decrypted data shielded by the enclave and leverage efficient matching algorithms. Extensive experimental evaluation shows that SGX adds only limited overhead to insecure plaintext matching outside secure enclaves while providing much better performance and more powerful filtering capabilities than alternative software-only solutions. To the best of our knowledge, this work is the first to demonstrate the practical benefits of SGX for privacy-preserving CBR.

Machine learning is widely used in security-sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test time to evade detection. To overcome this limitation, adversary-aware learning algorithms have been developed, exploiting robust optimization and game-theoretical models to incorporate knowledge of potential adversarial data manipulations into the learning algorithm. Despite these techniques have been shown to be effective in some adversarial learning tasks, their adoption in practice is hindered by different factors, including the difficulty of meeting specific theoretical requirements, the complexity of implementation, and scalability issues, in terms of computational time and space required during training. In this work, we aim to develop secure kernel machines against evasion attacks that are not computationally more demanding than their non-secure counterparts. In particular, leveraging recent work on robustness and regularization, we show that the security of a linear classifier can be drastically improved by selecting a proper regularizer, depending on the kind of evasion attack, as well as unbalancing the cost of classification errors. We then discuss the security of nonlinear kernel machines, and show that a proper choice of the kernel function is crucial. We also show that unbalancing the cost of classification errors and varying some kernel parameters can further improve classifier security, yielding decision functions that better enclose the legitimate data. Our results on spam and PDF malware detection corroborate our analysis.

Machine learning is widely used in security-sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test time to evade detection. To overcome this limitation, adversary-aware learning algorithms have been developed, exploiting robust optimization and game-theoretical models to incorporate knowledge of potential adversarial data manipulations into the learning algorithm. Despite these techniques have been shown to be effective in some adversarial learning tasks, their adoption in practice is hindered by different factors, including the difficulty of meeting specific theoretical requirements, the complexity of implementation, and scalability issues, in terms of computational time and space required during training. In this work, we aim to develop secure kernel machines against evasion attacks that are not computationally more demanding than their non-secure counterparts. In particular, leveraging recent work on robustness and regularization, we show that the security of a linear classifier can be drastically improved by selecting a proper regularizer, depending on the kind of evasion attack, as well as unbalancing the cost of classification errors. We then discuss the security of nonlinear kernel machines, and show that a proper choice of the kernel function is crucial. We also show that unbalancing the cost of classification errors and varying some kernel parameters can further improve classifier security, yielding decision functions that better enclose the legitimate data. Our results on spam and PDF malware detection corroborate our analysis.

We present a technique for performing secure location verification of position claims by measuring the time-difference of arrival (TDoA) between a fixed receiver node and a mobile one. The mobile node moves randomly in order to substantially increase the difficulty for an attacker to make false messages appear genuine. We explore the performance and requirements of such a system in the context of verifying aircraft position claims made over the Automatic Dependent Surveillance - Broadcast (ADS-B) system through the use of simulation and find that it correctly detects false claims with a peak accuracy of over 97\textbackslash% for the most complex attack modelled; requiring only 75m of deviation between the reported position and the actual position in order for a false claim to be detected. We then report on our design for a mobile receiver and our construction of a prototype using low-cost COTS equipment. We discuss some additional benefits of incorporating a mobile node, examine the difficulties to be overcome and explore the applicability of the approach in other location verification use-cases.

Future transportation systems highly rely on the integrity of spatial information provided by their means of transportation such as vehicles and planes. In critical applications (e.g. collision avoidance), tampering with this data can result in life-threatening situations. It is therefore essential for the safety of these systems to securely verify this information. While there is a considerable body of work on the secure verification of locations, movement of nodes has only received little attention in the literature. This paper proposes a new method to securely verify spatial movement of a mobile sender in all dimensions, i.e., position, speed, and direction. Our scheme uses Doppler shift measurements from different locations to verify a prover's motion. We provide formal proof for the security of the scheme and demonstrate its applicability to air traffic communications. Our results indicate that it is possible to reliably verify the motion of aircraft in currently operational systems with an equal error rate of zero.

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.