One is the issue of privacy protection. One of the major dilemmas facing big data ethics is that the application of related technologies in various fields such as politics, economy, culture, society and ecology should be based on the basic concept and rule of open *** enjoyment, which at the same time brings ethical dilemmas in terms of personal privacy and security protection. From an ethical point of view, privacy is the situation and state of affairs in private life that are not willing to be brought to the attention of the public society or known or interfered by other people who are not willing to do so. It is thus clear that personal privacy mainly involves the problem of demarcating the boundary between the private and public spheres. In the context of big data, the collection and processing of personal information are two major aspects of privacy ethics. In terms of personal information collection, privacy risk arises mainly from real-time monitoring of individual behavior by video surveillance, network surveillance, etc., and this is mainly due to the fact that the process of collection of personal information by surveillance is often unknown to the individual being monitored. For example, does the various data of students, patients, citizens, consumers, etc. collected through various platforms by schools, hospitals, governments, enterprises, etc. violate their personal privacy? In addition, the accumulation, comparison, analysis, and other processing of massive amounts of fragmented personal information may also result in threats and harm to personal privacy. Originally a variety of separate, unrelated small data, after processing, reorganization, editing and a series of processing, its own value will produce from quantitative to qualitative changes in the important changes, at the same time, its combination of the composition of the big data can also be depicted in the individual's characteristics, identity, habits, etc., and here there is likely to be people do not want to disclose the individual's privacy information.

The second is the issue of data neutrality. Data is a representation of those facts, concepts and instructions that can be processed by manual or automated means, and is the numerical value on which various calculations, studies or designs are based, including numbers, characters, symbols, charts and so on. Although Big Data itself is objective and neutral, it may also produce non-neutral results in data processing and use, and this is mainly due to the presence of various human subjective factors in the process. In today's explosive growth of global data, it is precisely because people are biased in the process of utilizing technology that ethical issues such as algorithmic discrimination continue to emerge. For example, algorithmic recommendation has become increasingly mature with the rapid development of big data and artificial intelligence, but it has also inevitably brought about the ethical issues of bias and discrimination. In real life, some online shopping platforms usually use personalized recommendation algorithms, such as the "recommended" page or the gift of "red packets" and "coupons". In essence, algorithm design is purposeful and value-based, and it reflects the intention and choice of the design subject. Therefore, the algorithm based on the individual wealth gap, gender differences, health status and other information comprehensive grasp, can be personalized, differentiated recommendation of related products or services, but will also lead to different groups of people in the information mastery level of inequity, and even "big data to kill ripe" and other algorithmic discrimination phenomenon.

Third, the digital divide. The digital divide is a new social equity issue arising in the digital era. The digital divide in the traditional sense mainly refers to the huge difference in the use of digital technology in terms of data accessibility, data application, data analysis, etc., which is manifested in the fact that a part of the group is able to better access and use digital technology, but another part of the group is difficult to access and use digital technology. The imbalance in the distribution of digital technology resources generated by the digital divide will gradually give rise to group conflicts and social injustice. In the context of big data, with the popularization of mobile Internet, the digital divide and the resulting social equity problems are no longer mainly manifested in the accessibility and application of digital technologies, but increasingly evolve into the data divide, and are centrally manifested in the skills divide and value divide caused by knowledge, technology, economy and other factors. Some government departments, enterprises, research institutions, etc., are able to obtain and use data more easily, and grasp individual behaviors in society through mining, computing, storing, and transmitting data, while it is difficult for the general public to obtain huge amounts of high-quality data. Furthermore, even if the public succeeds in acquiring large amounts of high-quality data, many of them do not have the skills to analyze all kinds of complex data. As a result, the digital divide here will grow wider and wider, and unfair ethical issues arise in terms of data access, technology adoption, and value compartmentalization.