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Data integrity in distributed data sensing and processing platforms or middlewares is an important issue, especially if those platforms are open to anyone. To leverage the resources of participating nodes and to enhance the scalability, nodes can be included in the data processing, e.g., in the aggregation of results. In an open system, it is also likely that some participating nodes are malicious and lie about their sensed values or about the results of data processed by them. Current approaches that preserve data integrity for in-network processing require expensive cryptographic operations. With Accountant we propose a new approach, which requires significantly less computation at the expense of slightly more signalling overhead. Furthermore, our approach cannot only preserve data integrity, but also allows to identify malicious nodes. For that, Accountant uses multiple inner node-disjoint trees for data dissemination and hash trees for preserving the data integrity. We compare it to existing solutions, showing that with only minor additional messaging overhead, Accountant can protect the data integrity and can identify attackers at the same time.

Accountant: Protection of Data Integrity and Identification of Malicious Nodes in In-network Data Processing