安全高效的网络内精确SUM查询处理

Stavros Papadopoulos, A. Kiayias, D. Papadias
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引用次数: 30

摘要

网络内聚合是无线传感器网络中采用的一种流行方法,它减少了在传感器读数上处理聚合查询(如SUM, MAX等)的能量消耗。最近,研究主要集中在网络内的安全聚合上,其动机是:(1)传感器通常部署在开放和不安全的环境中,以及(2)新的趋势,如外包,将聚合过程委托给不可信的服务。这个新范例需要以下关键的安全属性:数据机密性、完整性、身份验证和新鲜度。关于该主题的大多数现有工作要么不适合大规模传感器网络,要么只提供SUM查询的近似答案(以及它们的衍生物,例如COUNT, AVG等)。此外,目前还没有一种方法可以同时提供保密性和完整性。为此,我们提出了一种新颖而高效的方案,称为SIES。SIES是第一个支持安全的网络内处理精确SUM查询的解决方案,满足所有安全属性。它通过同态加密和秘密共享的结合来实现这一目标。此外,SIES是轻量级的(它依赖于廉价的哈希操作和模块化的加法/乘法),并且具有非常小的带宽消耗(按几个字节的顺序)。因此,对于资源受限的传感器,sis是一种理想的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Secure and efficient in-network processing of exact SUM queries
In-network aggregation is a popular methodology adopted in wireless sensor networks, which reduces the energy expenditure in processing aggregate queries (such as SUM, MAX, etc.) over the sensor readings. Recently, research has focused on secure in-network aggregation, motivated (i) by the fact that the sensors are usually deployed in open and unsafe environments, and (ii) by new trends such as outsourcing, where the aggregation process is delegated to an untrustworthy service. This new paradigm necessitates the following key security properties: data confidentiality, integrity, authentication, and freshness. The majority of the existing work on the topic is either unsuitable for large-scale sensor networks, or provides only approximate answers for SUM queries (as well as their derivatives, e.g., COUNT, AVG, etc). Moreover, there is currently no approach offering both confidentiality and integrity at the same time. Towards this end, we propose a novel and efficient scheme called SIES. SIES is the first solution that supports Secure In-network processing of Exact SUM queries, satisfying all security properties. It achieves this goal through a combination of homomorphic encryption and secret sharing. Furthermore, SIES is lightweight (it relies on inexpensive hash operations and modular additions/multiplications), and features a very small bandwidth consumption (in the order of a few bytes). Consequently, SIES constitutes an ideal method for resource-constrained sensors.
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