"Little Bird Cloud" is the cloud computing brand of Shenzhen Qianhai Little Bird Cloud Computing Co., Ltd., a leading enterprise-level cloud computing service provider in China. The team has many years of industry experience and focuses on the research and development of cloud computing technology. It provides comprehensive cloud computing solutions based on intelligent cloud servers for developers, government and enterprise users, financial institutions, etc., and provides users with reliable enterprise-level public cloud services.
The frequent data breaches every year always bring some lessons, one of which is that it is never too late to start taking data protection measures. Fortunately, businesses are showing greater focus on data privacy efforts, and big data is one of their top areas of concern.
Just yesterday, five former Microsoft employees said in an interview with Reuters that Microsoft's vulnerability report data had been illegally intruded in 2013, but this incident was has not been exposed.
Former employees of Microsoft said that it took Microsoft more than a month to fix all the security vulnerabilities listed in the compromised database, so the leaked vulnerability information will not have much impact on users of Windows products. influence. Microsoft also hired a third-party company to investigate the incident to understand whether there were attackers on the network using the leaked vulnerability information to launch attacks, but the company did not find any attacks related to the relevant vulnerabilities. .
Mary Shacklett is president of Transworld Data, a technology research and market development company. As an industry insider, she offers some advice to corporate executives to ensure they adopt solid data privacy practices for their big data.
One way to achieve anonymization is to encrypt personally identifiable data elements. Another approach is to integrate data into a larger data analysis by identifying data from individuals of similar value and then averaging it into a composite benefit value. Other methods include data redaction or masking.
Collecting digital information generated by governments, businesses and individuals creates tremendous opportunities for knowledge- and information-based decision-making. Driven by mutual benefit, data exchange and release can be carried out between parties in need. However, in its original form the data often contains sensitive personal information, and releasing it would violate personal privacy. Privacy protection under collective data release is an important and challenging problem. Most existing techniques use generalization and global deletion methods, while we propose a partial (local) deletion method to anonymize aggregate data. This method ensures that no matter how much prior knowledge the attacker has, strong association rules about sensitive information will no longer appear in the anonymized data. This method not only greatly reduces information loss, but also provides the option of maintaining the original data distribution or protecting useful association rules that can be mined according to the requirements of downstream usage scenarios. Preliminary evaluation shows that compared with classic methods, our method is more than 100 times better than other methods in maintaining the original data distribution, retains a larger number of useful association rules that can be mined, and only introduces a few false rules. Information loss is reduced by about 30% on average.
The above is only part of the data privacy work. There are more ways to protect data privacy, such as identifying the departments involved in big data within the company and regularly reviewing the data privacy of these departments. Finally, when formulating and implementing data privacy protection measures, they need to be based on the business needs and development of the enterprise.