Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency

Recently, several practical attacks raised serious concerns over the security of searchable encryption. The attacks have brought emphasis on forward privacy, which is the key concept behind solutions to the adaptive leakage-exploiting attacks, and will very likely to become mandatory in the design of new searchable encryption schemes. For a long time, forward privacy implies inefficiency and thus most existing searchable encryption schemes do not support it. Very recently, Bost (CCS 2016) showed that forward privacy can be obtained without inducing a large communication overhead. However, Bost's scheme is constructed with a relatively inefficient public key cryptographic primitive, and has a poor I/O performance. Both of the deficiencies significantly hinder the practical efficiency of the scheme, and prevent it from scaling to large data settings. To address the problems, we first present FAST, which achieves forward privacy and the same communication efficiency as Bost's scheme, but uses only symmetric cryptographic primitives. We then present FASTIO, which retains all good properties of FAST, and further improves I/O efficiency. We implemented the two schemes and compared their performance with Bost's scheme. The experiment results show that both our schemes are highly efficient, and FASTIO achieves a much better scalability due to its optimized I/O

Sequence-Oriented Diagnosis of Discrete-Event Systems

Model-based diagnosis has always been conceived as set-oriented, meaning that a candidate is a set of faults, or faulty components, that explains a collection of observations. This perspective applies equally to both static and dynamical systems. Diagnosis of discrete-event systems (DESs) is no exception: a candidate is traditionally a set of faults, or faulty events, occurring in a trajectory of the DES that conforms with a given sequence of observations. As such, a candidate does not embed any temporal relationship among faults, nor does it account for multiple occurrences of the same fault. To improve diagnostic explanation and support decision making, a sequence-oriented perspective to diagnosis of DESs is presented, where a candidate is a sequence of faults occurring in a trajectory of the DES, called a fault sequence. Since a fault sequence is possibly unbounded, as the same fault may occur an unlimited number of times in the trajectory, the set of (output) candidates may be unbounded also, which contrasts with set-oriented diagnosis, where the set of candidates is bounded by the powerset of the domain of faults. Still, a possibly unbounded set of fault sequences is shown to be a regular language, which can be defined by a regular expression over the domain of faults, a property that makes sequence-oriented diagnosis feasible in practice. The task of monitoring-based diagnosis is considered, where a new candidate set is generated at the occurrence of each observation. The approach is based on three different techniques: (1) blind diagnosis, with no compiled knowledge, (2) greedy diagnosis, with total knowledge compilation, and (3) lazy diagnosis, with partial knowledge compilation. By knowledge we mean a data structure slightly similar to a classical DES diagnoser, which can be generated (compiled) either entirely offline (greedy diagnosis) or incrementally online (lazy diagnosis). Experimental evidence suggests that, among these techniques, only lazy diagnosis may be viable in non-trivial application domains

Influence of surface properties on the electrical conductivity of silicon nanomembranes

Because of the large surface-to-volume ratio, the conductivity of semiconductor nanostructures is very sensitive to surface chemical and structural conditions. Two surface modifications, vacuum hydrogenation (VH) and hydrofluoric acid (HF) cleaning, of silicon nanomembranes (SiNMs) that nominally have the same effect, the hydrogen termination of the surface, are compared. The sheet resistance of the SiNMs, measured by the van der Pauw method, shows that HF etching produces at least an order of magnitude larger drop in sheet resistance than that caused by VH treatment, relative to the very high sheet resistance of samples terminated with native oxide. Re-oxidation rates after these treatments also differ. X-ray photoelectron spectroscopy measurements are consistent with the electrical-conductivity results. We pinpoint the likely cause of the differences

PyPose v0.6: The Imperative Programming Interface for Robotics

PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has been used in many tasks due to its meticulously designed application programming interface (API) and efficient implementation. From its initial launch in early 2022, PyPose has experienced significant enhancements, incorporating a wide variety of new features into its platform. To satisfy the growing demand for understanding and utilizing the library and reduce the learning curve of new users, we present the fundamental design principle of the imperative programming interface, and showcase the flexible usage of diverse functionalities and modules using an extremely simple Dubins car example. We also demonstrate that the PyPose can be easily used to navigate a real quadruped robot with a few lines of code

Fault Diagnosis of Discrete-Event Systems from Abstract Observations

Active systems (ASs) are a special class of (asynchronous) discrete-event systems (DESs). An AS is represented by a network of components, where each component is modeled as a communicating automaton. Diagnosing a DES amounts to finding out possible faults based on the DES model and a sequence of observations gathered while the DES is being operated. This is why the diagnosis engine needs to know what is observable in the behavior of the DES and what is not. The notion of observability serves this purpose. In the literature, defining the observability of a DES boils down to qualifying the state transitions of components either as observable or unobservable, where each observable transition manifests itself as an observation. Still, looking at the way humans observe reality, typically by associating a collection of events with a single, abstract perception, the state-of-the-art notion of DES observability appears somewhat narrow. This paper presents, a generalized notion of observability, where an observation is abstract rather than concrete, since it is associated with a DES behavioral scenario rather than a single component transition. To support the online diagnosis engine, knowledge compilation is performed offline. The outcome is a set of data structures, called watchers, which allow for the tracking of abstract observations

Diagnosis of Deep Discrete-Event Systems

An abduction-based diagnosis technique for a class of discrete-event systems (DESs), called deep DESs (DDESs), is presented. A DDES has a tree structure, where each node is a network of communicating automata, called an active unit (AU). The interaction of components within an AU gives rise to emergent events. An emergent event occurs when specific components collectively perform a sequence of transitions matching a given regular language. Any event emerging in an AU triggers the transition of a component in its parent AU. We say that the DDES has a deep behavior, in the sense that the behavior of an AU is governed not only by the events exchanged by the components within the AU but also by the events emerging from child AUs. Deep behavior characterizes not only living beings, including humans, but also artifacts, such as robots that operate in contexts at varying abstraction levels. Surprisingly, experimental results indicate that the hierarchical complexity of the system translates into a decreased computational complexity of the diagnosis task. Hence, the diagnosis technique is shown to be (formally) correct as well as (empirically) efficient

On-Line Diagnosis of Discrete-Event Systems: A Hierarchical Approach

Abstract-Hierarchy is a remedy way to reduce the demanding complexity of model-based diagnosis. In this paper, an approach to diagnosis of discrete-event systems in a hierarchical way is proposed, inspired by the concept "D-holon" and the concept "Silent Closure" presented in the literatures recently. Each extended silent closure can be seen as a special type of D-holons, called SCL-D-holon. Every hierarchical level is an SCL-D-holon built off line. When on line diagnosing a discrete-event system, only related SCL-D-holons will be called instead of all the SCL-D-holons generally, thus the space complexity is reduced. In comparison to on line creating silent closures, the efficiency is improved as well. Index Terms-Model-based diagnosis, discrete-event systems, hierarchy, on line

Diagnosis of Active Systems with Abstract Observations and Compiled Knowledge

An active system (AS) is a discrete-event system (DES) with asynchronous behavior, which is represented by a network of components that are modeled as communicating automata. When being operated, an AS performs a trajectory within its behavior space, while generating a sequence of observations, namely a temporal observation. The model of the AS and a temporal observation are the two key ingredients of the diagnosis task, which aims to find out possible faulty behavior via abductive reasoning. Among other knowledge, such reasoning requires knowing what is observable and what is not. This essential distinction constitutes the observability of the AS. In the literature, the observability of a DES boils down to qualifying each state transition either as observable or unobservable, which contrasts with the way humans observe reality, typically by mapping a collection of observations to a single, abstract perception. Moreover, the occurrence of single state transitions is not necessarily what we can observe or what we want to observe for diagnosis purposes. This paper presents an extended notion of observability, where each observation is associated with a behavioral scenario rather than a single state transition, where a scenario is defined as a regular language on state transitions. To speed up the online diagnosis engine, specific diagnosis-oriented knowledge is compiled offline. Eventually, the diagnosis technique based on abstract observability is extended to cope with temporal uncertainty

Diagnosis of complex active systems with uncertain temporal observations

Complex active systems have been proposed as a formalism for modeling real dynamic systems that are organized in a hierarchy of behavioral abstractions. As such, they constitute a conceptual evolution of active systems, a class of discrete-event systems introduced into the literature two decades ago. A complex active system is a hierarchy of active systems, each one characterized by its own behavior expressed by the interaction of several communicating automata. The interaction between active systems within the hierarchy is based on special events, which are generated when specific behavioral patterns occur. Recently, the task of diagnosis of complex active systems has been studied, with an efficient diagnosis technique being proposed. However, the observation of the system is assumed to be linear and certain, which turns out to be an over-assumption in real, large, and distributed systems. This paper extends diagnosis of complex active systems to cope with uncertain temporal observations. An uncertain temporal observation is a DAG where nodes are marked by candidate labels (logical uncertainty), whereas arcs denote partial temporal ordering between nodes (temporal uncertainty). By means of indexing techniques, despite the uncertainty of temporal observations, the intrinsic efficiency of the diagnosis task is retained in both time and space

Diagnosis of Temporal Faults in Discrete-Event Systems

Model-based diagnosis of discrete-event systems (DESs) generates a set of candidates upon the reception of a temporal observation. In the literature, a candidate is a set of faults produced by a trajectory of the DES that is consistent with the temporal observation. As such, a candidate does not convey any temporal relationship between faults, nor does it account for multiple occurrences of the same fault. To overcome the limitations of this set-oriented approach to diagnosis of DESs, the novel notions of temporal fault and temporal diagnosis are proposed, along with two diagnosis techniques. A temporal fault is the (possibly unbounded) sequence of faults produced by a trajectory. A temporal diagnosis is a (possibly infinite) set of temporal faults. Hence, in this new temporal-oriented approach to diagnosis of DESs, a candidate is a temporal fault. The fact that a temporal diagnosis turns out to be a regular language is key to coping with the infinity of candidates, which can be represented by a regular expression. The diagnosis task can be performed either by restricting the DES space to the trajectories that are consistent with the temporal observation, or by exploiting a temporal diagnoser which allows for fast online diagnosis. The claim of this paper is that the extra temporal information embedded in candidates may be essential in taking critical decisions based on the diagnosis results