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As a new factor of production in the era of digital economy, data realizes value creation in the flow and sharing, and at the same time, it also faces huge risk of leakage. Electric power data involves regulation and control data, marketing data, production equipment data, human and property management data, etc., which are vital tangible assets of enterprises. Data security and privacy protection are the core issues in the process of power data application and value mining. The problem of privacy disclosure in the process of data publishing for the purpose of information sharing and data mining is also increasingly prominent, so how to effectively protect private sensitive information from disclosure while realizing information sharing is particularly important. At present, there are three main methods to protect private data: (1) k-anonymity. The most widely used technology now is k-anonymity. When enterprises publish user data, they will hide the unique identity such as ID number or name to protect users' privacy. However, this method cannot resist link attack and background knowledge attack; (2) Data disruption. This method uses technologies such as scrambling, distortion and randomization to disrupt the original data, making the data lose authenticity and integrity, and the attacker cannot obtain the real data, but the availability of the data is greatly reduced; (3) Data encryption. Combining cryptography with data security, we use homomorphic encryption, asymmetric encryption technology and other mechanisms to form distributed security computing to support the work of privacy protection. For example, secure multi-party computing, but the problem of this method is that it requires too much computing resources and costs too much. |
IEEE/ICACT20230212 Slide.03
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