Privacy Implications of In-Network Aggregation Mechanisms for VANETs

Research on vehicular ad hoc networks (VANETs) is active and ongoing. Proposed applications range from safety applications, and traffic efficiency applications to entertainment applications. Common to many applications is the need to disseminate possibly privacy-sensitive information, such as location and speed information, over larger distances. In-network aggregation is a promising technology that can help to make such privacy-sensitive information only available in the direct vicinity of vehicles instead of communicating it over larger areas. Further away, only aggregated information that is not privacy-relevant anymore will be known. At the same time, aggregation mechanisms help to cope with the limited available wireless bandwidth. However, the exact privacy properties of aggregation mechanisms have still not been thoroughly researched. In this paper, we propose a metric to measure privacy enhancements provided by in-network aggregation and use it to compare existing schemes

Modeling In-Network Aggregation in VANETs

The multitude of applications envisioned for vehicular ad hoc networks requires efficient communication and dissemination mechanisms to prevent network congestion. In-network data aggregation promises to reduce bandwidth requirements and enable scalability in large vehicular networks. However, most existing aggregation schemes are tailored to specific applications and types of data. Proper comparative evaluation of different aggregation schemes is difficult. Yet, comparability is essential to properly measure accuracy, performance, and efficiency. We outline a modeling approach for VANET aggregation schemes to achieve objective comparability. Our modeling approach consists of three models, which provide different perspectives on an aggregation scheme. The generalized architecture model facilitates categorization of aggregation schemes. The aggregation information flow model supports analysis of where information is aggregated by a scheme. The aggregation state graph models how knowledge about the road network and its environment is represented by a scheme. Furthermore, it facilitates error estimation with respect to the ground truth. We apply each modeling approach to existing aggregation schemes from the literature and highlight strengths, as well as weaknesses, that can be used as a starting point for designing a more generic aggregation scheme

Energy efficient clustering and secure data aggregation in wireless sensor networks

Communication consumes the majority of a wireless sensor network\u27s limited energy. There are several ways to reduce the communication cost. Two approaches used in this work are clustering and in-network aggregation. The choice of a cluster head within each cluster is important because cluster heads use additional energy for their responsibilities and that burden needs to be carefully distributed. We introduce the energy constrained minimum dominating set (ECDS) to model the problem of optimally choosing cluster heads in the presence of energy constraints. We show its applicability to sensor networks and give an approximation algorithm of O(log n) for solving the ECDS problem. We propose a distributed algorithm for the constrained dominating set which runs in O(log n log [triangle]) rounds with high probability. We show experimentally that the distributed algorithm performs well in terms of energy usage, node lifetime, and clustering time and thus is very suitable for wireless sensor networks. Using aggregation in wireless sensor networks is another way to reduce the overall communication cost. However, changes in security are necessary when in- network aggregation is applied. Traditional end-to-end security is not suitable for use with in-network aggregation. A corrupted sensor has access to the intermediate data and can falsify results. Additively homomorphic encryption allows for aggregation of encrypted values, with the result being the same as the result as if unencrypted data were aggregated. Using public key cryptography, digital signatures can be used to achieve integrity. We propose a new algorithm using homomorphic encryption and additive digital signatures to achieve confidentiality, integrity and availability for in- network aggregation in wireless sensor networks. We prove that our digital signature algorithm which is based on Elliptic Curve Digital Signature Algorithm (ECDSA) is at least as secure as ECDSA. Even without in-network aggregation, security is a challenge in wireless sensor networks. In wireless sensor networks, not all messages need to be secured with the same level of encryption. We propose a new algorithm which provides adequate levels of security while providing much higher availablility [sic] than other security protocols. Our approach uses similar amounts of energy as a network without security --Abstract, page iv

Analysis of the End-by-Hop Protocol for Secure Aggregation in Sensor Networks

In order to save bandwidth and thus battery power, sensor network measurements are sometimes aggregated en-route while being reported back to the querying server. Authentication of the measurements then becomes a challenge if message integrity is important for the application. At ESAS 2007, the End-by-Hop protocol for securing in-network aggregation for sensor nodes was presented. The solution was claimed to be secure and efficient and to provide the possibility of trading off bandwidth against computation time on the server. In this paper, we disprove these claims. We describe several attacks against the proposed solution and point out shortcomings in the original complexity analysis. In particular, we show that the proposed solution is inferior to a naive solution without in-network aggregation both in security and in efficiency

Securing Smart Grid In-Network Aggregation through False Data Detection

Existing prevention-based secure in-network data aggregation schemes for the smart grids cannot e ectively detect accidental errors and falsified data injected by malfunctioning or compromised meters. In this work, we develop a light-weight anomaly detector based on kernel density estimator to locate the smart meter from which the falsified data is injected. To reduce the overhead at the collector, we design a dynamic grouping scheme, which divides meters into multiple interconnected groups and distributes the verification and detection load among the root of the groups. To enable outlier detection at the root of the groups, we also design a novel data re-encryption scheme based on bilinear mapping so that data previously encrypted using the aggregation key is transformed in a form that can be recovered by the outlier detectors using a temporary re-encryption key. Therefore, our proposed detection scheme is compatible with existing in-network aggregation approaches based on additive homomorphic encryption. We analyze the security and eÿciency of our scheme in terms of storage, computation and communication overhead, and evaluate the performance of our outlier detector with experiments using real-world smart meter consumption data. The results show that the performance of the light-weight detector yield high precision and recall