Sparkplug [1] is an emergent open-source software specification for Industrial Internet of Things (IIoT) systems, designed to favor data integration and device interoperability in an MQTT infrastructure. Although the security issues of IIoT systems can have relevant safety implications, Sparkplug only provides basic security features and essential, coarse-grained access control (AC) mechanisms. Effective AC solutions for Sparkplug-based IIoT systems still need to be designed, and, due to the Sparkplug’s increasing popularity and its recent definition as an ISO Standard [1], this has become a crucial need. To fill this void, this paper proposes an approach to efficiently enforcing fine-grained AC in Sparkplug-based IIoT systems. In particular, we define a fine-grained discretionary AC model and a related reference monitor implementing an efficient enforcement mechanism. Early performance evaluations show a reasonably low time overhead.

Recent emergencies, such as the COVID-19 pandemic have shown how timely information sharing is essential to promptly and effectively react to emergencies. Internet of Things has magnified the possibility of acquiring information from different sensors and using it for emergency management and response. However, it has also amplified the potential of information misuse and unauthorized access to information by untrusted users. Therefore, this paper proposes an access control framework tailored to MQTT-based IoT ecosystems. By leveraging Complex Event Processing, we can enforce controlled and timely data sharing in emergency and ordinary situations. The system has been tested with a case study that targets patient monitoring during the COVID-19 pandemic, showing promising results.

Data confidentiality and privacy are becoming primary concerns for Internet of Things applications. A variety of access control approaches have been proposed to address this issue. In this demonstration we present a tool, called Mammoth, which complements an ABAC framework for MQTT-based IoT ecosystems, with a dashboard of analysis services designed for security administrators. Mammoth supports the real-time analysis of target MQTT ecosystems, allowing security administrators to analyze the effects of the enforcement mechanisms on the flow of exchanged messages. The demonstration will allow participants to try Mammoth services in a simulated MQTT-based scenario.

Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a reference data model and related data manipulation languages. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.

Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a standard data model and related data manipulation language. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.

Confidentiality and privacy of data managed by IoT ecosystems is becoming a primary concern. This paper targets the design of a general access control enforcement mechanism for MQTT-based IoT ecosystems. The proposed approach is presented with ABAC, but other access control models can be similarly supported. The solution is based on an enforcement monitor that has been designed to operate as a proxy between MQTT clients and an MQTT server. The monitor enforces access control constraints by intercepting and possibly manipulating the flow of exchanged MQTT control packets. Early experimental evaluations have overall shown low enforcement overhead.

NoSQL datastores allow the efficient management of high volumes of heterogeneous and unstructured data, meeting the requirements of a variety of today ICT applications. However, most of these systems poorly support data security, and recent surveys show that their simplistic support for data protection is considered as a reason not to use them.1 In recent years, Attribute Based Access Control (ABAC) is getting more and more popularity, for its ability to provide highly flexible and customized forms of data protection at different granularity levels. In the current work, with the aim to raise users' confidence in the protection of data managed by NoSQL systems, we define a general approach to enforce ABAC within NoSQL systems. Our approach relies on SQL++[20], a unifying query language for NoSQL platforms. In particular, we develop a novel SQL++ query rewriting mechanism able to enforce heterogeneous types of ABAC policies specified up to cell level. Experimental results show an overhead which is not negligible for policies covering high percentage of the fields characterizing the protected documents, but which is far more contained when field level policies are more sparsely specified.

Privacy has become a key requirement for data management systems. Nevertheless, NoSQL datastores, namely highly scalable non relational database management systems, which often support data management of Internet scale applications,still do not provide support for privacy policies enforcement. With this work, we begin to address this issue, by proposing an approach for the integration of purpose based policy enforcement capabilities into MongoDB, a popular NoSQL datastore. Our contribution consists of the enhancement of the MongoDB role based access control model with privacy concepts and related enforcement monitor. The proposed monitor is easily integrable into any MongoDB deployment through simple configurations. Experimental results show that our monitor enforces purpose-based access control with low overhead.

Many modern applications use context related information to provide highly personalized services, and use NoSQL databases for data management, as these systems show outstanding performance and support high volumes of data. However, NoSQL databases integrate poor data protection features with basic coarse grained access control and no support for context aware policies. Therefore, we believe that a general approach is required to enhance NoSQL datastores with fine grained context aware access control. In this paper, we start to fill this void by targeting MongoDB, a very popular datastore. The contribution is twofold. We enhance MongoDB's access control model with advanced features and we define an enforcement monitor for the proposed enhanced model, which can be straightforwardly used in any MongoDB deployment. Technological limitations of MongoDB do not allow implementing the same efficient enforcement mechanism for all query types. As a consequence, experimental results show an enforcement overhead that is significant for aggregate queries, which contrasts with a low overhead measured for find and map-reduce queries.

Although database management systems (DBMSs) enforce access control according to a variety of models (see [2] for an overview), the majority of them do not integrate native privacy protection mechanisms. This void has been partially filled out with the advent of purpose based access control, as this access control model has brought to the integration of basic privacy preservation functionalities into DBMSs. Even though purposes represent a key feature of privacy policies, DBMSs' privacy awareness can be significantly increased considering additional privacy related aspects. With this work we do a step to achieve this goal by focusing on the actions performed by queries on data and the categories of the accessed data. We propose an access control model that supports highly customized privacy-aware access control policies and significantly improves the basic privacy preservation capabilities of the purpose based model. The proposed model is complemented with an efficient enforcement monitor, which can be easily integrated into relational DBMSs. Early experimental evaluations show the efficiency of the proposed framework.

The recent years have seen the birth of several NoSQL datastores, which are getting more and more popularity for their ability to handle high volumes of heterogeneous and unstructured data in a very efficient way. In several cases, NoSQL databases proved to outclass in terms of performance, scalability, and ease of use relational database management systems, meeting the requirements of a variety of today ICT applications. However, recent surveys reveal that, despite their undoubted popularity, NoSQL datastores suffer from some weaknesses, among which the lack of effective support for data protection appears among the most serious ones. Proper data protection mechanisms are therefore required to fill this void. In this work, we start to address this issue by focusing on access control and discussing the definition of a fine-grained access control framework for document-oriented NoSQL datastores. More precisely, we first focus on issues and challenges related to the definition of such a framework, considering theoretical, implementation, and integration aspects. Then, we discuss the reasons for which state-of-the-art fine-grained access control solutions proposed for relational database management systems cannot be used within the NoSQL scenario. We then introduce possible strategies to address the identified issues, which are at the basis of the framework development. Finally, we shortly report the outcome of an experience where the proposed framework has been used to enhance the data protection features of a popular NoSQL database.

Executing data analytics tasks in MapReduce systems introduces new security and privacy concerns as the processed unstructured datasets may contain sensitive information (e.g., social security numbers, business sensitive information) at the level of individual records, and the existing file-level access control mechanisms provide all or nothing access to the entire dataset. To address these concerns, we propose GUARDMR which is a novel, modular framework that can enforce fine-grained security policies at the key-value level in MapReduce systems. The presented security policies can dynamically create authorized views of data resources based on the organizational roles of the MapReduce users. GUARDMR further simplifies the specification of authorized views via automatically generating the bytecode of the functions necessary for creating the views, from the high level specification language (i.e., OCL). It facilitates enforcement of a broad, flexible set of policies that can handle the complexity demanded by high volume, high variety, unstructured datasets and general MapReduce computation without any modification to the underlying MapReduce system and operating system. Our evaluation results indicate that GUARDMR provides fine-grained access control for Apache Hadoop system with easy maintainability and very low overhead

Among the variety of access control models proposed for database management systems (DBMSs) a key role is covered by the purpose-based access control model, which, while enforcing access control, also achieves basic privacy preservation. We believe that DBMSs could greatly take benefit from the integration of an enhanced purpose based model supporting highly customized and efficient access control. Therefore, in this paper, we propose a purpose-based model that supports action-aware policy specification and a related efficient enforcement framework to be integrated into relational DBMSs. The experimental evaluation we have performed shows the feasibility and efficiency of the proposed framework.

Within Database Management Systems (DBMS), privacy policies regulate the collection, access and disclosure of the stored personal, identifiable and sensitive data. Policies often specify obligations which represent actions that must be executed or conditions that must be satisfied before and/or after data are accessed. Although numerous policies specification languages allow the specification, no systematic support is provided to enforce obligations within relational DBMS. In this paper, we make a step to fill this void presenting an approach to the definition of an enforcement monitor which handles privacy policies that include obligations. Such a monitor is derived from the same set of policies that must be enforced, and regulates the execution of SQL code based on the satisfaction of a variety of obligation types. The proposed solution is systematic, has been automated, does not require any programming activity and can be used with most of the existing relational DBMSs.

Privacy is becoming a key requirement for ICT applications that handle personal data. However, Database Management Systems (DBMSs), which are devoted to data collection and processing by definition, still do not provide the proper support for privacy policies. Policies are enforced by ad-hoc programmed software modules that complement DBMS access control services. This practice is time consuming, error prone, and neither general nor scalable. This work does a first step to overcome these limits. We propose a systematic approach to the automatic development of a monitor that regulates the execution of SQL queries based on purpose based privacy policies. The proposed solution does not require programming, it is general, platform independent and usable with most of the existing relational DBMSs.

Problem frames are an approach to requirements modeling that is gaining increasing attention and popularity. A few meta-models have already been proposed to precisely define the notation and -in some cases- to support the construction of tools. However, the meta-models proposed till now concentrate on modeling the single problem frame, without addressing the whole problem. This is particularly limiting, since one of the strengths of the problem frames approach is in tackling the complexity of problems by guiding their decomposition into sub-problems, and in identifying recurring patterns of elementary problems. In order to support this fundamental activity, a meta-model has to support the notion of sub-problems and sub-domains, moreover it must provide suitable means to represent problems in intermediate situations during the (de)composition phases. This paper presents a meta-model that solves the aforementioned problems and provides a suitable base for the construction of a tool based on the EMF/GEF technology.

Problem frames are an approach to requirements modeling that is gaining increasing attention and popularity. The approach provides useful concepts and methodological guidelines. However, problem frames are not equipped with an expressive and complete notation and they lack tools support. These limitations can be addressed by introducing a suitable meta-model to formally define the notation. In this way it is also possible to identify the aspects that are not covered by the problem frames notation and to provide hooks for user-defined extensions. The meta-model is also expected to support the underlying analysis methodology. Furthermore, it can provide the basis for building a tool supporting both the editing of problem frames and the other activities associated with the approach (frame concern, composition, etc.). This paper presents a meta-model that addresses the former issues and was used for building a tool with the EMF/GMF technology.

Recently, SysML has been adopted by the Object Management Group as a modelling language for Systems Engineering. SysML is a UML profile that represents a subset of UML 2 with extensions. A wide adoption of the language could be hindered by the lack of a methodology that drives the modelling activities. Problem Frames (PFs) are a rigorous approach to requirements modelling that has the potential to improve the software development process. Unfortunately, PFs are not supported by an intuitive notation and easy to use tools. As a consequence, their adoption in industry is limited. This paper explores the possibility of exploiting the PFs ideas in the context of SysML models. The goal is to provide model-based development processes using SysML with a set of concepts and guidelines that are sound and have already been used and validated.