科研之友
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摘要
Secure data aggregation schemes are widely adopted in wireless sensor networks, not only to minimize the energy and bandwidth consumption, but also to enhance the security. Statistics obtained from data aggregation schemes often fall into three categories, i.e., distributive, algebraic, and holistic. In practice, a wide range of reasonable aggregation queries are combinations of several different statistics. Providing multi-functional aggregation support is also a primary demand for data preprocessing in data mining. However, most existing secure aggregation schemes only focus on a single type of statistics. Some statistics, especially holistic ones (e.g., median), are often difficult to compute efficiently in a distributed mode even without considering the security issue. In this paper, we first propose a new Multi-functiOnal secure Data Aggregation scheme (MODA), which encodes raw data into well-defined vectors to provide value-preservation, order-preservation and context-preservation, and thus offering the building blocks for multi-functional aggregation. A homomorphic encryption scheme is adopted to enable in-ciphertext aggregation and end-to-end security. Then, two enhanced and complementary schemes are proposed based on MODA, namely, RandOm selected encryption based Data Aggregation (RODA) and COmpression based Data Aggregation (CODA). RODA can significantly reduce the communication cost at the expense of slightly lower but acceptable security on a leaf node, while CODA can dramatically reduce communication cost with the lower aggregation accuracy. The performance results obtained from theoretic analysis and experimental evaluation of three real datasets under different scenarios, demonstrate that our schemes can achieve the performance superior to the most closely related work.
