Biblio

Real-time editing tools like Google Docs, Microsoft Office Online, or Etherpad have changed the way of collaboration. Many of these tools are based on Operational Transforms (OT), which guarantee that the views of different clients onto a document remain consistent over time. Usually, documents and operations are exposed to the server in plaintext – and thus to administrators, governments, and potentially cyber criminals. Therefore, it is highly desirable to work collaboratively on encrypted documents. Previous implementations do not unleash the full potential of this idea: They either require large storage, network, and computation overhead, are not real-time collaborative, or do not take the structure of the document into account. The latter simplifies the approach since only OT algorithms for byte sequences are required, but the resulting ciphertexts are almost four times the size of the corresponding plaintexts. We present SECRET, the first secure, efficient, and collaborative real-time editor. In contrast to all previous works, SECRET is the first tool that (1.) allows the encryption of whole documents or arbitrary sub-parts thereof, (2.) uses a novel combination of tree-based OT with a structure preserving encryption, and (3.) requires only a modern browser without any extra software installation or browser extension. We evaluate our implementation and show that its encryption overhead is three times smaller in comparison to all previous approaches. SECRET can even be used by multiple users in a low-bandwidth scenario. The source code of SECRET is published on GitHub as an open-source project:https://github.com/RUB-NDS/SECRET/

Self-describing key-value data formats such as JSON are becoming increasingly popular as application developers choose to avoid the rigidity imposed by the relational model. Database systems designed for these self-describing formats, such as MongoDB, encourage users to use denormalized, heavily nested data models so that relationships across records and other schema information need not be predefined or standardized. Such data models contribute to long-term development complexity, as their lack of explicit entity and relationship tracking burdens new developers unfamiliar with the dataset. Furthermore, the large amount of data repetition present in such data layouts can introduce update anomalies and poor scan performance, which reduce both the quality and performance of analytics over the data. In this paper we present an algorithm that automatically transforms the denormalized, nested data commonly found in NoSQL systems into traditional relational data that can be stored in a standard RDBMS. This process includes a schema generation algorithm that discovers relationships across the attributes of the denormalized datasets in order to organize those attributes into relational tables. It further includes a matching algorithm that discovers sets of attributes that represent overlapping entities and merges those sets together. These algorithms reduce data repetition, allow the use of data analysis tools targeted at relational data, accelerate scan-intensive algorithms over the data, and help users gain a semantic understanding of complex, nested datasets.

Self-describing key-value data formats such as JSON are becoming increasingly popular as application developers choose to avoid the rigidity imposed by the relational model. Database systems designed for these self-describing formats, such as MongoDB, encourage users to use denormalized, heavily nested data models so that relationships across records and other schema information need not be predefined or standardized. Such data models contribute to long-term development complexity, as their lack of explicit entity and relationship tracking burdens new developers unfamiliar with the dataset. Furthermore, the large amount of data repetition present in such data layouts can introduce update anomalies and poor scan performance, which reduce both the quality and performance of analytics over the data. In this paper we present an algorithm that automatically transforms the denormalized, nested data commonly found in NoSQL systems into traditional relational data that can be stored in a standard RDBMS. This process includes a schema generation algorithm that discovers relationships across the attributes of the denormalized datasets in order to organize those attributes into relational tables. It further includes a matching algorithm that discovers sets of attributes that represent overlapping entities and merges those sets together. These algorithms reduce data repetition, allow the use of data analysis tools targeted at relational data, accelerate scan-intensive algorithms over the data, and help users gain a semantic understanding of complex, nested datasets.

The Internet of Things (IOT) is a network of networks where massively large numbers of objects or things are interconnected to each other through the network. The Internet of Things brings along many new possibilities of applications to improve human comfort and quality of life. Complex systems such as the Internet of Things are difficult to manage because of the emergent behaviours that arise from the complex interactions between its constituent parts. Our key contribution in the paper is a proposed multiagent web for the Internet of Things. Corresponding data management architecture is also proposed. The multiagent architecture provides autonomic characteristics for IOT making the IOT manageable. In addition, the multiagent web allows for flexible processing on heterogeneous platforms as we leverage off web protocols such as HTTP and language independent data formats such as JSON for communications between agents. The architecture we proposed enables a scalable architecture and infrastructure for a web-scale multiagent Internet of Things.