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Big data privacy protection

2019-12-05 来源: 51Due教员组 类别: Essay范文

下面为大家整理一篇优秀的essay代写范文- Big data privacy protection,供大家参考学习,这篇论文讨论了大数据隐私保护。大数据环境下,隐私面临前所未有的挑战,部分传统隐私保护技术面临失效,如何合理选择隐私保护技术是一个具有挑战性的任务。数据隐私保护的常用技术包括数据加密、匿名化以及数据溯源等技术,数据加密技术主要解决数据存储、计算以及通信的安全性,匿名化技术主要解决数据加工处理、挖掘分析以及数据发布时防止敏感信息泄露。

Big data privacy protection,大数据隐私保护,essay代写,作业代写,代写

The convergence of big data technology and economic society has led to the rapid growth of data, which has become the basic strategic resources of the country. While bringing huge benefits, big data also brings unprecedented challenges to user privacy protection.

The pair announced on November 28, 100 App personal information collection and the privacy policy evaluation report, 10 types of App are widespread suspicion of too much personal information collection, 59 App allegedly excessive collection "location", 28 App allegedly excessive collection "directory information", 23 App allegedly excessive collection "identity". Globally, in June 2018, the us company Exactis disclosed about 340 million records, involving the private information of 230 million people, because the database was exposed to the publicly accessible network and no effective security measures were taken.

Common technologies for data privacy protection include data encryption, anonymity and data traceability. Data encryption mainly deals with the security of data storage, computation and communication, while anonymity mainly deals with data processing, mining and analysis, and data release to prevent the disclosure of sensitive information.

At present, data encryption technology mainly includes secure multi-party computing, ciphertext retrieval, homomorphic encryption and other commonly used cryptography technology, mainly to solve the security of data storage, communication and analysis applications. Secure multi-party computing can solve the problem of collaborative computing between a group of participants who do not trust each other to protect their privacy and ensure that no additional information is exposed in addition to user input and output information. Encrypted storage and ciphertext provide high retrieval efficiency under the premise of higher security.

Privacy protection is the most commonly used technical means, usually using suppression, generalization and other operations to hide or obscure data and data sources. Generalization is refers to the description of data more generalization, abstraction, and suppression is not publish some data item, common data privacy protection model is K - Anonymity, L'm, T - closeness model, epsilon - differential model and its improved algorithm, such as privacy in order to satisfy the demands of different application scenarios.

Quasi-identifier refers to a user record that can be determined with a high probability by combining certain external information. The k-anonymization model requires a certain number of records in the published data that are indistinguishable from the quasi-identifier, making it impossible for potential attackers to distinguish the individuals to which the private information belongs. The disadvantage of k-anonymity is that the sensitive attribute in an equivalence class is not constrained. For example, if any sensitive attribute in an equivalence class has the same value, the attacker can deduce the sensitive value.

The l-diversity model requires each equivalence class to contain at least L different values of sensitive attributes on the basis of k-anonymity. Although l-diversity guarantees the Diversity of sensitive attributes, it ignores the global distribution of sensitive attributes. T - closeness model on the basis of l - diversification, considering the distribution of sensitive attributes, which requires sensitive all equivalence class attribute value distribution as far as possible close to the global distribution of properties.

Differential privacy is a rigorous and provable privacy protection model, and is a privacy protection parameter to adjust the data availability and privacy. In practical application, the difficulty and cost of implementation are high. In order to balance privacy and availability, the choice of parameter is a challenging problem.

Big data is characterized by large scale, multiple sources and dynamic update. Traditional privacy protection technologies may fail or face new challenges.

First, the challenges of data encryption. Many cryptographic technologies are based on in-memory computing, which are not suitable for the distributed storage and parallel computing environment of big data. They are faced with problems such as poor scalability, high computing cost and unsuitable for the new computing framework.

Second, the challenges of anonymising technology. Both the anonymization model and the differential privacy protection model assume that the data in the data set are independent of each other, and the large-scale, high-speed, diversity, relevance and fusion of multiple heterogeneous data sources of big data may invalidate the original privacy protection scheme.

Finally, big data analysis and fusion bring new challenges to privacy protection. New computing frameworks, high-performance algorithms and more complex analysis models can mine outlier points, frequent patterns, classification patterns, correlations between data and user behavior patterns in big data, thus revealing user privacy information or providing attackers with richer background knowledge.

At present, big data has a broad development prospect, but it also faces unprecedented privacy challenges and risks. Big data privacy protection is not only a technical issue, but also involves laws, regulations, regulatory models, religion and many other aspects, which need joint efforts from all walks of life to achieve.

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