High-level Petri Nets Research Articles

Taking Complete Finite Prefixes To High Level, Symbolically*

Unfoldings are a well known partial-order semantics of P/T Petri nets that can be applied to various model checking or verification problems. For high-level Petri nets, the so-called symbolic unfolding generalizes this notion. A complete finite prefix of a P/T Petri net’s unfolding contains all information to verify, e.g., reachability of markings. We unite these two concepts and define complete finite prefixes of the symbolic unfolding of high-level Petri nets. For a class of safe high-level Petri nets, we generalize the well-known algorithm by Esparza et al. for constructing small such prefixes. We evaluate this extended algorithm through a prototype implementation on four novel benchmark families. Additionally, we identify a more general class of nets with infinitely many reachable markings, for which an approach with an adapted cut-off criterion extends the complete prefix methodology, in the sense that the original algorithm cannot be applied to the P/T net represented by a high-level net.

Verification of Unary Communicating Datalog Programs

We study verification of reachability properties over Communicating Datalog Programs (CDPs), which are networks of relational nodes connected through unordered channels and running Datalog-like computations. Each node manipulates a local state database (DB), depending on incoming messages and additional input DBs from external services. Decidability of verification for CDPs has so far been established only under boundedness assumptions on the state and channel sizes, showing at the same time undecidability of reachability for unbounded states with only two unary relations or unbounded channels with a single binary relation. The goal of this paper is to study the open case of CDPs with bounded states and unbounded channels, under the assumption that channels carry unary relations only. We discuss the significance of the resulting model and prove the decidability of verification of variants of reachability, captured in fragments of first-order CTL. We do so through a novel reduction to coverability problems in a class of high-level Petri Nets that manipulate unordered data identifiers. We study the tightness of our results, showing that minor generalizations of the considered reachability properties yield undecidability of verification, both for CDPs and the corresponding Petri Net model.

Intelligent conflict detection of IoT services using high-level Petri nets

AbstractInternet of Things has become a common paradigm for various domains. To meet a user’s complex requirement, we should compose multiple IoT devices (IoT services) to provide comprehensive services to the user. However, these services usually coexist, which is likely to lead to conflicts. Moreover, each user may have different kinds of needs. Suppose that in a smart home there are more than one person, a conflict may occur when they request the same service in this environment. Actually, even though they request different IoT services, among which if there exists function impact or QoS impact, a conflict could still occur. In this paper, we propose to employ high-level Petri nets to detect conflicts among IoT services. We first model the formal methods for conflict policies. Then, we present a Petri nets-based mechanism for modeling and detecting conflicts. Finally, we expand the previous model and use high-level Petri nets for handling fuzzy IoT conflict policies modeling and reasoning. The experimental results show that the proposed approach performs well in accuracy.

SGO: Semantic Group Obfuscation for Location-Based Services in VANETS.

Location privacy is an important parameter to be addressed in the case of vehicular ad hoc networks. Each vehicle frequently communicates with location-based services to find the nearest location of interest. The location messages communicated with the location server may contain sensitive information like vehicle identity, location, direction, and other headings. A Location-Based Services (LBS) server is not a trusted entity; it can interact with an adversary, compromising the location information of vehicles on the road and providing a way for an adversary to extract the future location tracks of a target vehicle. The existing works consider two or three neighboring vehicles as a virtual shadow to conceal location information. However, they did not fully utilize the semantic location information and pseudonym-changing process, which reduces the privacy protection level. Moreover, a lot of dummy location messages are generated that increase overheads in the network. To address these issues, we propose a Semantic Group Obfuscation (SGO) technique that utilizes both location semantics as well as an efficient pseudonym-changing scheme. SGO creates groups of similar status vehicles on the road and selects random position coordinates for communication with the LBS server. It hides the actual location of a target vehicle in a vicinity. The simulation results verify that the proposed scheme SGO improves the anonymization and entropy of vehicles, and it reduces the location traceability and overheads in the network in terms of computation cost and communication cost. The cost of overhead is reduced by 55% to 65% compared with existing schemes. We also formally model and specify SGO using High-Level Petri Nets (HLPNs), which show the correctness and appropriateness of the scheme.

Modeling and controlling IoT-based devices’ behavior with high-level Petri nets

In the last decade Internet of Things (IoT) emerged as a concept for intelligent, flexible, autonomous, and distributed system while IoT-based devices have highlighted for agent communication, data analysis, and decision-making tasks. Integrating high-level behavioral and low-level control has capital significance for their implementation and is still a challenge. In the field of high-level behavior, Petri nets are widely applied for modeling, simulating, and analyzing concurrent, discrete-event and knowledge-based systems, furthermore even models are used as basis for control algorithm, direct controlling devices’ behavioral with high-level Petri nets is little diffused. This paper presents a control architecture used to integrate models in high-level Petri nets with IoT-based devices. Operational tests were provided using CPN Tools and micro-controlled physical devices. A study with a mobile robot is shown and results indicate the reasonability and applicability of this proposed approach in IoT-based devices.

Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS, SLAMSA, (p, k)-Angelization, and (p, l)-Angelization, but these were found to be insufficient in terms of robust privacy and performance. (p, l)-Angelization was successful against different privacy disclosures, but it was not efficient. To the best of our knowledge, no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records. In this paper, we suggest an improved version of (p, l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization. Fuzz-classification (p, l)-Angel uses artificial intelligence based fuzzy logic for classification, a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes. We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets. The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

SeSPHR A Methodology for Secure Sharing of Personal Health Records in the Cloud

The widespread acceptance of cloud-based services in the healthcare sector has resulted in cost effective and convenient exchange of Personal Health Records (PHRs) among several participating entities of the e-Health systems. Nevertheless, storing the confidential health information to cloud servers is susceptible to revelation or theft and calls for the development of methodologies that ensure the privacy of the PHRs. Therefore, we propose a methodology called SeSPHR for secure sharing of the PHRs in the cloud. The SeSPHR scheme ensures patient-centric control on the PHRs and preserves the confidentiality of the PHRs. The patients store the encrypted PHRs on the un-trusted cloud servers and selectively grant access to different types of users on different portions of the PHRs. A semi- trusted proxy called Setup and Re-encryption Server (SRS) is introduced to set up the public/private key pairs and to produce the re- encryption keys. Moreover, the methodology is secure against insider threats and also enforces a forward and backward access control. Furthermore, we formally analyze and verify the working of SeSPHR methodology through the High Level Petri Nets (HLPN). Performance evaluation regarding time consumption indicates that the SeSPHR methodology has potential to be employed for securely sharing the PHRs in the cloud. Key Words:: Access Control, Cloud Computing, Personal Health Records, Privacy.

DIFCS: A Secure Cloud Data Sharing Approach Based on Decentralized Information Flow Control

Summary In the field of cloud computing, collaboration and data sharing among clouds have dramatically increased. How to protect the security of shared cloud data is being an urgent problem. Based on decentralized information flow control (DIFC) model, this paper presents an approach, DIFC-based data sharing approach (DIFCS), to protect both confidentiality and integrity of shared cloud data. Additionally, this paper designs a privilege protection policy for DIFCS, resulting in its capability of preventing malicious users from modifying privilege. The correctness and security properties of DIFCS are proved by formal analysis and verification, where firstly, DIFCS is formally interpreted into high-level petri net (HLPN) representation and analyzed using Z language, then is automatically verified with SMT-Lib and Z3 solver. The formal analysis and verification results reveal that DIFCS holds the security properties of confidentiality, integrity, authenticity, and privilege tamper-proof. The experimental results further demonstrate the high efficiency of DIFCS.

Towards a formal modelling, analysis and verification of a clone node attack detection scheme in the internet of things

A clone node attack is one of the severe attacks on the Internet of Things (IoT) network. In a clone node attack, an adversary aims to physically capture the secret credentials of the deployed IoT devices to make the clone of devices look similar to the original devices. In recent years, several solutions for detecting clone node attacks have been designed; however, the focus of such designed solutions is on system design discussion, specific feature sets, high-level abstraction of underlying system architecture and fulfilling the performance analysis requirements. While the designed solutions efficiently detect clone attacks, critical features such as modelling, analysis, and verification of such solutions are frequently overlooked, necessitating that users verify their internal behaviour and claimed properties to ensure that no problematic scenarios or anomalies exist. Taking into account the rationale for formal verification and the limitations of previous works, this paper aims to perform formal modelling, analysis, and verification of our existing proposed clone node attack detection scheme for IoT. We used the High-Level Petri Nets (HLPNs) and Z-specification language to model our proposed scheme and analyse the logic correctness of our scheme using incidence markings and confidence interval matrices. To validate the functional correctness, we utilised the Satisfiability Modulo Theories Library (SMT-Lib) and the Z3 Solver and verified the functional satisfiable properties by analysing sat/unsat verification results and their execution time. Finally, we extend our modelling work by utilising the Coloured Petri Nets (CPNs), taking into consideration their both timed and untimed models with the aim of validating functional correctness and logical correctness, respectively. To analyse both correctnesses of CPN models, we used logging facility statistics such as count, sum, average, minimum, and maximum as data packet observations.

A Distributed Mix-Context-Based Method for Location Privacy in Road Networks

Preserving location privacy is increasingly an essential concern in Vehicular Adhoc Networks (VANETs). Vehicles broadcast beacon messages in an open form that contains information including vehicle identity, speed, location, and other headings. An adversary may track the various locations visited by a vehicle using sensitive information transmitted in beacons such as vehicle identity and location. By matching the vehicle identity used in beacon messages at various locations, an adversary learns the location history of a vehicle. This compromises the privacy of the vehicle driver. In existing research work, pseudonyms are used in place of the actual vehicle identity in the beacons. Pseudonyms should be changed regularly to safeguard the location privacy of vehicles. However, applying simple change in pseudonyms does not always provide location privacy. Existing schemes based on mix zones operate efficiently in higher traffic environments but fail to provide privacy in lower vehicle traffic densities. In this paper, we take the problem of location privacy in diverse vehicle traffic densities. We propose a new Crowd-based Mix Context (CMC) privacy scheme that provides location privacy as well as identity protection in various vehicle traffic densities. The pseudonym changing process utilizes context information of road such as speed, direction and the number of neighbors in transmission range for the anonymisation of vehicles, adaptively updating pseudonyms based on the number of a vehicle neighbors in the vicinity. We conduct formal modeling and specification of the proposed scheme using High-Level Petri Nets (HPLN). Simulation results validate the effectiveness of CMC in terms of location anonymisation, the probability of vehicle traceability, computation time (cost) and effect on vehicular applications.

Inter‐cloud secure data sharing and its formal verification

AbstractWith the development of cloud computing, data sharing and collaboration have become increasing among cloud‐oriented organizations. In addition to trivial management work, data providers face many difficulties in managing access control policies in cloud data sharing, to prevent permission abuse and information leakage. This article mainly answers the following question: is it possible to achieve verifiably secure inter‐cloud data sharing? This article proposes an inter‐cloud data sharing (ICDS) approach based on ciphertext‐policy attribute‐based encryption (CP‐ABE) to ensure the security of ICDS. The security properties that ICDS holds are formally verified, where the information flow rules are defined and used in the verification. The information flow of the proposed ICDS is firstly modeled by use of high‐level Petri nets (HLPN) formally, then being represented in Z language, and at last the security properties of ICDS are verified by the Z3 solver. The formal analysis proves that the ICDS holds the security properties of confidentiality, authenticity, and collusion‐resistance.

Robust Shipping Date Predictions in Manufacturing using High-level Petri Nets

ABSTRACT Machine failures, unavailability of the workforce, and missing raw materials are typical, frequent faults in complex manufacturing systems and cause unstable processes. Plans and schedules become volatile, predictability of shipping dates suffers and delayed delivery appears. However, for a customer reliable shipping can often be of higher benefit than promised short-term shipping with significant probability of delay and error. Inserting buffer times stabilises schedules by reducing the potential impact of disruptions. Therefore, computing appropriately sized buffer times for individual jobs in a flexible job shop manufacturing with setup times is a promising yet difficult mission for production research. In this paper, we discuss methods for buffer time calculation that are based on data collected directly from the underlying production processes. We show how a high-level Petri net model can be used to simulate stochastic variations of the manufacturing process. Using this simulation model, we estimate the effect of buffer times and produce predictions for the expected shipping dates together with probabilities for how likely the predicted dates will actually be met.

Formal verification and complexity analysis of confidentiality aware textual clinical documents framework

Smart health-care is the innovation that leads to enhanced diagnostic tools, improved patient treatment, and gadgets that ease the quality of life for majority of people. Textual clinical documents about an individual contain sensitive and semantically corelated terms. Most privacy-preserving approaches are not designed to prevent confidentiality threats. Although, recent approaches improved the utility of published output with generalized terms retrieved from several medical and general-purpose knowledge bases like SNOMED-CT and MASH. However, these models work on predefined sensitive terms using Wikipedia articles instead of authentic benchmarks. These Information Content-based methods are not capable to achieve the best balance between privacy and utility. The existing approaches guarantee syntactic privacy by sanitization but lack semantic privacy for textual clinical data. Therefore, it is imperative to design a confidentiality-aware framework to overcome these problems. Our proposed Confidentiality aware Textual Clinical Data Framework use preprocessed combinations of the terms instead of all combinations and perform automatic detection and sanitization of the sensitive and semantically correlated terms. The probabilistic sampling-based method guarantees the semantic privacy. We use high-level Petri nets to perform formal modeling of our proposed approach. Furthermore, we have also performed a detailed complexity analysis of the proposed framework.

A Petri net extension for systems of concurrent communicating agents with durable actions

This paper provides a true-concurrency approach for the specification and verification of systems of concurrent communicating agents with durable actions. We present high-level Petri nets with durable actions (DaHL) to cope with various details in such complex systems. We define a DaHL module as an open variant of time-dependent colored Petri nets. A DaHL system is a fused set of modules for systems consisting of concurrent agents which can interact with each other. We also introduce hybrid-based reachability graph that covers the entire state space of DaHL systems with a true-concurrency semantics. We show that such reachability graph allows us to check important properties such as deadlock-freeness, liveness, home space, and reversibility, and also to predict timing properties prior to real implementation. A case study is used to model and analyze a simple scenario where autonomous vehicles are able to transport containers freely in an enterprise environment.

Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs.

Location privacy is a critical problem in the vehicular communication networks. Vehicles broadcast their road status information to other entities in the network through beacon messages. The beacon message content consists of the vehicle ID, speed, direction, position, and other information. An adversary could use vehicle identity and positioning information to determine vehicle driver behavior and identity at different visited location spots. A pseudonym can be used instead of the vehicle ID to help in the vehicle location privacy. These pseudonyms should be changed in appropriate way to produce uncertainty for any adversary attempting to identify a vehicle at different locations. In the existing research literature, pseudonyms are changed during silent mode between neighbors. However, the use of a short silent period and the visibility of pseudonyms of direct neighbors provides a mechanism for an adversary to determine the identity of a target vehicle at specific locations. Moreover, privacy is provided to the driver, only within the RSU range; outside it, there is no privacy protection. In this research, we address the problem of location privacy in a highway scenario, where vehicles are traveling at high speeds with diverse traffic density. We propose a Dynamic Grouping and Virtual Pseudonym-Changing (DGVP) scheme for vehicle location privacy. Dynamic groups are formed based on similar status vehicles and cooperatively change pseudonyms. In the case of low traffic density, we use a virtual pseudonym update process. We formally present the model and specify the scheme through High-Level Petri Nets (HLPN). The simulation results indicate that the proposed method improves the anonymity set size and entropy, provides lower traceability, reduces impact on vehicular network applications, and has lower computation cost compared to existing research work.

Simulating Train Dispatching Logic with High-Level Petri Nets

Simulating Train Dispatching Logic with High-Level Petri Nets

Availability analysis and maintainability optimization of an offshore wind farm using high-level Petri nets

Availability analysis and maintainability optimization of an offshore wind farm using high-level Petri nets

Privacy preservation of electronic health records with adversarial attacks identification in hybrid cloud

An increasing trend in healthcare organizations to outsource EHRs’ data to the cloud highlights new challenges regarding the privacy of given individuals. Healthcare organizations outsource their EHRs data in a hybrid cloud that elevates the problem of security and privacy in terms of EHRs’ access to an unlimited number of recipients in a hybrid cloud environment. In this paper, we investigated the need for a privacy-preserving access control model for the hybrid cloud. A comprehensive and exploratory analysis of privacy-preserving solutions with the help of taxonomy for cloud-based EHRs is described in this work. We have formally identified the existence of internal access control and external privacy disclosures in outsourcing system architecture for hybrid cloud. Then, we proposed a privacy-preserving XACML based access control model (PPX-AC) that supports fine-grained access control with the multipurpose utilization of EHRs alongside state-of-the-art privacy mechanism. Our proposed approach invalidates the identified security and privacy attacks. We have formally verified the proposed privacy-preserving XACML based access control model (PPX-AC) with the invalidation of identified privacy attacks using High-Level Petri Nets (HLPN). Moreover, property verification of the proposed model in SMT-lib and Z3 solver and implementation of the model proves its effectiveness in terms of privacy-aware EHRs access and multipurpose usage.

A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive attributes

Privacy preserving data publishing of electronic health record (EHRs) for 1 to M datasets with multiple sensitive attributes (MSAs) is an interesting and challenging issue. There is always a trade-off between privacy and utility in data publishing. Most of the privacy-preserving models shows critical privacy disclosure issues and, hence, they are not robust in practical datasets. The k-anonymity model is a broadly used privacy model to analyze privacy disclosures, however, this model is only useful against identity disclosure. To address the limitations of k-anonymity, a group of privacy model extensions have been proposed in past years. It includes a p-sensitive k-anonymity model, a p+-sensitive k-anonymity model, and a balanced p+-sensitive k-anonymity model. However these privacy-preserving models are not sufficient to preserve the privacy of end-users in practical datasets. In this paper we have formalize the behavior of an adversary which perform identity and attribute disclosures on balanced p+-sensitive k-anonymity model with the help of adversarial scenarios. Since balanced p+-sensitive k-anonymity model is not sufficient for 1 to M with MSAs datasets privacy preservation. We propose an extended privacy model called “1: M MSA-(p, l)-diversity” for 1: M dataset with MSAs. We then perform formal modeling and verification of the proposed model using High-Level Petri Nets (HLPN) to confirm privacy attacks invalidation. Experimental results show that our proposed “1: M MSA-(p, l)-diversity model” is efficient and provide enhanced data utility of published data.

A structural approach to graph transformation based on symmetric Petri nets

Graph Transformation Systems (GTS) and Petri Nets (PN) are two central, theoretically sound, formal models for concurrent or distributed systems. A lot of papers have focused on the relationship between GTS and PN. It is generally accepted that PN are instances of GTS due to the lack of ability to adapt or reconfigure their structure. In this paper, which extends a recent one, we reverse this perspective by presenting a formal definition of GTS in terms of Symmetric Nets (SN). SN are a standard High-Level PN formalism featuring a compact syntax which highlights behavioral symmetries of systems. The major strength lies in the possibility of using well-established tools to edit/efficiently analyze (SN-based) graph transformation models. We follow a structural approach, based on a newly implemented symbolic calculus, to validate Graph Transformation Rules (GTR) and formally verify GTS properties.In the second part of the paper, we supply a semantic characterization of the SN-based graph transformation model, by carrying out a thorough comparison with the classical, algebraic double-pushout (DPO) approach. We constructively and formally show how to translate any DPO rule to a corresponding, elementary or composite, SN rule. We treat both injective and non-injective DPO rules/rule matches. The comparison shows that the SN approach is, in some sense, a generalization of the DPO.