Research on security and privacy protection of electric power data sharing

doi:10.3969/j.issn.2097-0706.2024.05.004

Abstract

Abstract:

Electric power data can reflect the development status of society and is promising in the opening and integrated applications. In order to create and release the value of power data， power companies need to build a data sharing service system for all industries. However， there are security issues such as privacy leakage， data tampering and shortage of data security aggregation method in the process of power data sharing. At present， there are three main technologies that can deal with the security problems above， blockchain， privacy computing and desensitization. Based on reviews on relevant literatures， a demand model for and privacy data protection in power data sharing is proposed. The three security and privacy protection technologies for electric power data sharing are summarized，and their integration and applications in different scenarios are comparatively analyzed. Different security and privacy protection schemes provide references for promoting electric power data opening and sharing.

Key words: electricity data, open sharing, data security, blockchain, privacy computing, desensitization

CLC Number:

TK018：TM73

XING Huidi, GONG Gangjun, ZHAI Mingyue, LIU Xuesong, WANG Haomiao, YANG Shuang. Research on security and privacy protection of electric power data sharing[J]. Integrated Intelligent Energy, 2024, 46(5): 30-40.

Figures/Tables 9

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来源	脱敏对象	脱敏方法	脱敏程度	优点	缺点
文献[28]	电力数据	将电力敏感数据进行分类处理，依据分类结果分配不同的脱敏方法	较高	可以针对不同类型的数据采用适当的脱敏算法	对数据的原始格式和精度产生影响，影响数据的可用性
文献[29]	电力用户身份信息	使用k-means聚类方法筛选出电力敏感数据，再使用脱敏规则进行脱敏	高	有效保证用户身份信息安全	脱敏后的数据可用性差
文献[30]	各类电力数据	采用机器学习识别将数据中的敏感数据识别出来并将敏感数据隔离，只向需求者提供非敏感数据	高	具有灵活性和通用性	技术处于起步阶段不够完善
文献[31]	配电网通信隐私数据	使用XGBoost算法将配电网隐私数据进行分类，分类之后对每一个类别使用不同的脱敏方法	较高	准确性、敏感度和脱敏效率高	只能对固定类型的敏感数据进行识别
文献[32]	电力用户地址信息	将用户地址信息进行截断处理之后，对地址经纬度信息进行脱敏，然后对脱敏后的经纬度进行反分析，得出新的地址	较高	脱敏之后的地址信息依旧具有一定的应用价值	目前这种方法只能应用于测试环境，并不能应用于生产环境中

来源	区块链技术	安全防护/隐私保护手段	加密算法	安全性	隐私性	计算效率
文献[41]	联盟链	数据经非对称加密后进行共享，并将数据存储在区块链中，防止数据篡改	非对称加密	一般	较高	一般
文献[42]	联盟链	源数据加密后使用加密手段进行处理	Paillier加密+BLS短签名	一般	一般	一般
文献[43]	私有链+联盟链	隐私数据在私有链中存储计算，再由联盟链进行共享	非对称加密	一般	较高	较低
文献[441]	联盟链	使用多重加密和交互认证机制保证数据防篡改防伪造	非对称加密+Hash算法	高	一般	低
文献[45]	联盟链	使用区块链的分布式特点保障隐私数据安全	属性加密	高	较高	一般

融合方法	来源	隐私计算技术	隐私保护手段	加密算法	隐私性	可追溯性
隐私计算技术	文献[57]	FL	各电力数据采集端点将训练好的FL模型经过模型加密模块上传到云端，进行多轮FL得到全局模型	同态加密/函数加密/差分隐私/安全多方计算	高	较低
隐私计算技术	文献[59]	FL+MPC	模型训练时原始数据存储在本地，使用同态加密对数据进行加密计算，加密后再用FL模型训练数据	同态加密	高	较低
隐私计算技术+区块链	文献[60]	TEE	将可信执行环境嵌入到区块链系统中，数据计算在可信执行环境中进行	非对称加密	高	高
	文献[61]	同态加密	将同态加密技术应用到区块链系统中，能够在密态下对数据进行共享计算	同态加密技术	高	高
	文献[62]	MPC	将MPC应用到区块链中，使用MPC在本地进行数据整合	同态加密	高	高
	文献[63]	MPC	利用差分隐私保障隐私安全，利用区块链保障数据安全性	差分隐私	高	高