Biblio
Proof systems for verifiable computation (VC) have the potential to make cloud outsourcing more trustworthy. Recent schemes enable a verifier with limited resources to delegate large computations and verify their outcome based on succinct arguments: verification complexity is linear in the size of the inputs and outputs (not the size of the computation). However, cloud computing also often involves large amounts of data, which may exceed the local storage and I/O capabilities of the verifier, and thus limit the use of VC. In this paper, we investigate multi-relation hash & prove schemes for verifiable computations that operate on succinct data hashes. Hence, the verifier delegates both storage and computation to an untrusted worker. She uploads data and keeps hashes; exchanges hashes with other parties; verifies arguments that consume and produce hashes; and selectively downloads the actual data she needs to access. Existing instantiations that fit our definition either target restricted classes of computations or employ relatively inefficient techniques. Instead, we propose efficient constructions that lift classes of existing arguments schemes for fixed relations to multi-relation hash & prove schemes. Our schemes (1) rely on hash algorithms that run linearly in the size of the input; (2) enable constant-time verification of arguments on hashed inputs; (3) incur minimal overhead for the prover. Their main benefit is to amortize the linear cost for the verifier across all relations with shared I/O. Concretely, compared to solutions that can be obtained from prior work, our new hash & prove constructions yield a 1,400x speed-up for provers. We also explain how to further reduce the linear verification costs by partially outsourcing the hash computation itself, obtaining a 480x speed-up when applied to existing VC schemes, even on single-relation executions.
To this date the majority of the existing instruments to measure trustworthiness in an online context are based on Likert scaling [1,3,11]. These however are somewhat restricted in applicability. Statements formed in Likert scaling are typically addressing one specific website. Therefore, adjusting these statements for other websites can be accompanied with a loss of validity. To meet these limitations, we propose to use semantic differential. Research has shown that using semantic differential is appropriate to measure multidimensional constructs [8,12] such as trust. Our novel approach in measuring trustworthiness exceeds Likert based scaling in its effortless application in different online context and its better translatability. After one pre-study and two online-studies with a total of 554 participants we achieved to develop a questionnaire with nine items which is comparable to other existing questionnaires in terms of reliability and internal consistency. But it overcomes the limitation of Likert scale based questionnaire.
In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically measured, i.e. quantitative information. In this study, we focus on the quantitative opinion, propose an uncertainty-aware approach called Kernel Density Estimation from Multiple Sources (KDEm) to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that KDEm not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.
Modern mobile systems such as smartphones, tablets, and wearables contain a plethora of sensors such as camera, microphone, GPS, and accelerometer. Moreover, being mobile, these systems are with the user all the time, e.g., in user's purse or pocket. Therefore, mobile sensors can capture extremely sensitive and private information about the user including daily conversations, photos, videos, and visited locations. Such a powerful sensing capability raises important privacy concerns. To address these concerns, we believe that mobile systems must be equipped with trustworthy sensor notifications, which use indicators such as LED to inform the user unconditionally when the sensors are on. We present Viola, our design and implementation of trustworthy sensor notifications, in which we leverage two novel solutions. First, we deploy a runtime monitor in low-level system software, e.g., in the operating system kernel or in the hypervisor. The monitor intercepts writes to the registers of sensors and indicators, evaluates them against checks on sensor notification invariants, and rejects those that fail the checks. Second, we use formal verification methods to prove the functional correctness of the compilation of our invariant checks from a high-level language. We demonstrate the effectiveness of Viola on different mobile systems, such as Nexus 5, Galaxy Nexus, and ODROID XU4, and for various sensors and indicators, such as camera, microphone, LED, and vibrator. We demonstrate that Viola incurs almost no overhead to the sensor's performance and incurs only small power consumption overhead.
In the age of the IoT (Internet of Things), a random number generator plays an important role of generating encryption keys and authenticating a piece of an embedded equipment. The random numbers are required to be uniformly distributed statistically and unpredictable. To satisfy the requirements, a physical true random number generator (TR-NG) is used. In this paper, we implement a TRNG using an SR latch on 40 nm CMOS ASIC. This TRNG generates the random number by exclusive ORing (XORing) the outputs of 256 SR latches. We evaluate the random number generated using statistical tests in accordance with BSI AIS 20/31 and using an IID (Independent and Identically Distributed) test, and the entropy estimation in accordance with NIST SP800-90B changing the supply voltage and environmental temperature within its rated values. As a result, the TRNG passed all the tests except in a few cases. From this experiment, we found that the TRNG has a robustness against environmental change. The power consumption is 18.8 micro Watt at 2.5 MHz. This TRNG is suitable for embedded systems to improve security in IoT systems.
In this research, we aim to suggest a method for designing trustworthy PRVAs (product recommendation virtual agents). We define an agent's trustworthiness as being operated by user emotion and knowledgeableness perceived by humans. Also, we suggest a user inner state transition model for increasing trust. To increase trust, we aim to cause user emotion to transition to positive by using emotional contagion and to cause user knowledgeableness perceived to become higher by increasing an agent's knowledge. We carried out two experiments to inspect this model. In experiment 1, the PRVAs recommended package tours and became highly knowledgeable in the latter half of ten recommendations. In experiment 2, the PRVAs recommended the same package tours and expressed a positive emotion in the latter half. As a result, participants' inner states transitioned as we expected, and it was proved that this model was valuable for PRVA recommendation.
With the advent of the Internet of Things (IoT), billions of devices are expected to continuously collect and process sensitive data (e.g., location, personal health). Due to limited computational capacity available on IoT devices, the current de facto model for building IoT applications is to send the gathered data to the cloud for computation. While private cloud infrastructures for handling large amounts of data streams are expensive to build, using low cost public (untrusted) cloud infrastructures for processing continuous queries including on sensitive data leads to concerns over data confidentiality. This paper presents STYX, a novel programming abstraction and managed runtime system, that ensures confidentiality of IoT applications whilst leveraging the public cloud for continuous query processing. The key idea is to intelligently utilize partially homomorphic encryption to perform as many computationally intensive operations as possible in the untrusted cloud. STYX provides a simple abstraction to the IoT developer to hide the complexities of (1) applying complex cryptographic primitives, (2) reasoning about performance of such primitives, (3) deciding which computations can be executed in an untrusted tier, and (4) optimizing cloud resource usage. An empirical evaluation with benchmarks and case studies shows the feasibility of our approach.
Modern vehicles rely on a variety of electronic systems and components. One of those components is the vehicle key. Today, a key typically implements at least three functions: mechanical locking with a key blade, the electronic immobilizer to autorise the start of the engine, and the remote keyless entry (RKE) system that allows to wirelessly (un)lock the doors and disable the alarm system. These main components of a vehicle key are shown in Figure 1. For the mechanical part of the vehicle key, it is well known that the key blade can be easily copied and that the locking cylinder can be bypassed with other means (using so-called "decoders" or simply a screwdriver). In contrast, immobilizer and RKE rely on wireless protocols to cryptographically authenticate the vehicle key to the car. Immobilizers employ radio frequency identification (RFID) transponders to carry out a challenge-response protocol over a low-range bidirectional link at a frequency of 125 kHz. In the past, researchers have revealed severe aws in the cryptography and protocols used by immobilizers, leading to the break of the major systems Megamos, Hitag2, and DST40 [7, 6, 1]. In contrast to the immobilizer, the RKE part uses unidirectional communication (the vehicle only receives, the key fob only transmits) over a high-range wireless link with operating distances of tens to one hundred meters. These systems are based on rolling codes, which essentially transmit a counter (that is incremented on each button press) in a cryptographically authenticated manner. Until recently, the security of automotive RKE had been scrutinized to a lesser degree than that of immobilizers, even though vulnerabilities in similar systems have been known since 2008 with the attacks on KeeLoq [3]. Other results reported in the literature include an analytical attack on a single, outdated vehicle [2] and the so-called "RollJam" technique [5], which is based on a combination of replay and selective jamming. In 2016, it was shown that severe aws exist in the RKE systems of major automotive manufacturers [4]. On the one hand, the VWgroup (Volkswagen, Seat, Skoda, Audi) based the security of their RKE system on a few global cryptographic keys, potentially affecting hundreds of million vehicles world-wide. By extracting these global keys from the firmware of electronic controls units (ECUs) once, an adversary is able to create a duplicate of the owner's RKE fob by eavesdropping a single rolling code. The second case study in [4] exposes new cryptographic weaknesses in the Hitag2 cipher when used for RKE. Applying a correlation-based attack, an adversary can recover the 48-bit cryptographic key by eavesdropping four to eight rolling codes and performing a one-minute computation on a standard laptop. Again, this attack affects millions of vehicle world-wide. Manufacturers that used Hitag2 in their RKE system include Alfa Romeo, Peugeot, Lancia, Opel, Renault, and Ford among others. In this keynote talk, we will present the results of [4] and put them in into a broader context by revisiting the history of attacks on RKE systems and automotive electronics.
A well known technique for embedding high dimensional objects in two or three dimensional space is the t-distributed stochastic neighbour embedding (t-SNE). The t-SNE minimizes the Kullback-Liebler (KL) divergence between two probability distributions, one induced on points in the high dimensional space and the other induced on points in the low dimensional embedding space. In this work, we consider a more general framework of using Rényi divergence which is parametrized by the order α, the KL-divergence is a special case when α → 1.We study how various Rényi divergences perform when compared to the KL-divergence. We show that in terms of the metrics of trustworthiness and neighbourhood preservation, the embedding becomes better as Rényi divergence approaches the KL-divergence.
Complex event processing has become an important technology for big data and intelligent computing because it facilitates the creation of actionable, situational knowledge from potentially large amount events in soft realtime. Complex event processing can be instrumental for many mission-critical applications, such as business intelligence, algorithmic stock trading, and intrusion detection. Hence, the servers that carry out complex event processing must be made trustworthy. In this paper, we present a threat analysis on complex event processing systems and describe a set of mechanisms that can be used to control various threats. By exploiting the application semantics for typical event processing operations, we are able to design lightweight mechanisms that incur minimum runtime overhead appropriate for soft realtime computing.