面向电力物联网流数据的一种具有隐私保护的KNN查询方法

Privacy-preserving KNN query method for streaming data in power Internet of Things

电力物联网是一个智慧服务系统,为人们提供了状态全面感知、信息高效处理、应用便捷灵活的服务,然而在享受服务的同时却面临着隐私泄露的风险。目前有关电力数据的隐私保护的成果主要集中在安全聚合,对于诸多基础服务的核心技术(如KNN查询)却鲜有涉及。与传统关系型数据不同的是,电力物联网采集的是用户用电的流数据,并且电力参数的各数据之间还具有动态相关性,攻击者可以通过数据挖掘等手段推测未来数据的变化趋势。为此,提出了一种具有隐私保护的KNN查询方法。首先,提出了基于桶距离的相似性度量模型,并证明了桶距离的相似性度量模型与基于欧氏距离的相似性度量模型的误差上界和下界;同时通过该模型,能将相似性度量转换为集合的交操作;构造了一种隐私保护函数,通过代入不同参数,可为各智能终端生成不同的数据隐私保护函数和查询隐私保护函数;在此基础上,提出了基于桶划分和随机数分配的数据编码方案,编码数据经过隐私保护函数加密后,具有密文不可区分的特点,能有效抵抗选择明文攻击、数据挖掘攻击、统计分析攻击、ICA攻击以及推理预测等攻击手段。分析和仿真表明,提出的安全KNN查询方法不仅具有较高的安全性,而且开销较低。

The power Internet of Things(PIoT)is a smart service system that offers full-state awareness,efficient information processing,and convenient and flexible applications to users.However,these services also pose a risk of privacy leakage.The existing research on privacy protection of power data mainly concentrates on secure aggregation,but seldom addresses the core technology of many basic services,such as KNN query.Unlike traditional relational data,the PIoT collects flowing data of user electricity consumption,and the various power parameters exhibit dynamic correlations.Attackers can use data mining and other methods to infer future trends in data changes.Therefore,this paper proposed a privacy-preserving KNN query method.Firstly,it proposed a similarity measurement model based on bucket distance,and proved the upper and lower bounds of the error between the similarity measurement model based on bucket distance and the similarity measurement model based on Euclidean distance.Through this model,the similarity measurement could be transformed into set intersection operations.Then,it constructed a privacy-preserving function,which could generate different data privacy-preserving functions and query privacy-preserving functions for various smart terminals by substituting different parameters.Based on this,it proposed a data encoding scheme based on bucket partitioning and random number allocation.After being encrypted by the privacy-preserving function,the encoded data possessed the characteristic of ciphertext indistinguishability,and could effectively resist various attacks such as chosen plaintext attacks,data mining attacks,statistical analysis attacks,ICA attacks,and inference prediction attacks.Analysis and simulation demonstrate that the proposed secure KNN query method not only has high security but also has low overhead.