User's Personal Privacy Research Articles

Research on Internet data security and privacy protection

The company's data leakage has always been a very serious problem. Many of the leaked data is the user's personal privacy data, which will bring a lot of trouble to users. In order to improve the security of the storage and use of personal private information in the database, two methods of lightweight private information encryption and social engineering anonymization are proposed to protect database information. We compare the proposed method with the application layer encryption and pre encryption methods. The experimental results show that the proposed method can effectively protect the user's personal privacy data, and can detect and recover the privacy data when managers need it. The storage efficiency of the three methods is close. Therefore, it has good application value in privacy protection.

Design of running training assistance system based on blockchain technology in wireless network

Running exercise can increase the basal metabolic rate and increase the time of aerobic exercise. Based on the current needs of the general public for running auxiliary training, this paper combines wireless sensing and blockchain technology in the design scheme, and designs and implements a running training auxiliary technology. First, it obtains the user's gait information and other related parameters in the process through the wireless sensor network, and optimize the calculation gait in different states through the noise processing algorithm. Then, we use the blockchain technology to design a data transmission and storage plan for the protection and analysis of the user's personal privacy data. The proposed method builds a new type of sports training assistance system for the masses of modern society and contributes to the masses' physical exercise.

Novel trajectory privacy-preserving method based on clustering using differential privacy

With the development of location-aware technology, a large amount of location data of users is collected by the trajectory database. If these trajectory data are directly used for data mining without being processed, it will pose a threat to the user's personal privacy. At the moment, differential privacy is favored by experts and scholars because of its strict mathematical rigor, but how to apply differential privacy technology to trajectory clustering analysis is a difficult problem. To solve the problems in which existing trajectory privacy-preserving models have poor data availability or difficulty to resist complex privacy attacks, we devise novel trajectory privacy-preserving method based on clustering using differential privacy. More specifically, Laplacian noise is added to the count of trajectory location in the cluster to resist the continuous query attack. Then, radius-constrained Laplacian noise is added to the trajectory location data in the cluster to avoid too much noise affecting the clustering effect. According to the noise location data and the count of noise location, the noise clustering center in the cluster is obtained. Finally, it is considered that the attacker can associate the user trajectory with other information to form secret reasoning attack, and secret reasoning attack model is proposed. And we use the differential privacy technology to give corresponding resistance. Experimental results using the open data show that the proposed algorithm can not only effectively protect the private information of the trajectory data, but also ensure the data availability in cluster analysis. And compared with other algorithms, our algorithm has good effect on some evaluation indicators.

Novel trajectory data publishing method under differential privacy

The existing location-based services have collected a large amount of user trajectory data, and if these data are directly released without any processing, the user's personal privacy will be leaked. At present, differential privacy protection technology is favored by many scholars, but how to apply it reasonably to location-based services is also a challenge for us. Trajectory is spatiotemporal continuous, but most existing methods only consider the single location of moving objects at a certain time without considering the entire trajectory, which may destroy the spatiotemporal integrity of the trajectory. In this paper, we address this problem and firstly propose a Sequence R (SR)-tree structure that satisfies the differential privacy based on the R-tree, and we construct the SR-Tree by using the trajectory sequence instead of the minimum bounding rectangle of the R-tree. Then we put forward an attack model called non-location sensitive information attack, in order to resist this attack, we add noise into the location data and non-location sensitive data using differential privacy techniques. Finally, the Algorithm can be consistently dealt with the problem of data inconsistency after adding noise. Experimental results show that our Algorithm not only has high data availability, operational efficiency, but also has good scalability.

Improving Security and Privacy Attribute Based Data Sharing in Cloud Computing

Data sharing is a convenient and economic service supplied by cloud computing. Data contents privacy also emerges from it since the data is outsourced to some cloud servers. To protect the valuable and sensitive information, various techniques are used to enhance access control on the shared data. In these techniques, Ciphertext-policy attribute-based encryption (CP-ABE) can make it more convenient and secure. Traditional CP-ABE focuses on data confidentiality merely, while the user's personal privacy protection is an important issue at present. CP-ABE with hidden access policy ensures data confidentiality and guarantees that user's privacy is not revealed as well. However, most of the existing schemes are inefficient in communication overhead and computation cost. Moreover, most of those works take no consideration about authority verification or the problem of privacy leakage in authority verification phase. To tackle the problems mentioned above, a privacy preserving CP-ABE scheme with efficient authority verification is introduced in this paper. Additionally, the secret keys of it achieve constant size. Meanwhile, the proposed scheme achieves the selective security under the decisional $n$ -BDHE problem and decisional linear assumption. The computational results confirm the merits of the presented scheme.

The Data Aggregation Privacy Protection Algorithm of Body Area Network Based on Data Partitioning

As a new network, the body area network is widely used in the field of health care, emergency rescue and so on. In the practical application, a large number of data collected by the body area network are closely related to user's personal privacy. However the body area network is an open environment. When transmitted in the network, the user's privacy data is vulnerable to the attacker's eavesdropping, tampering and other security threats. At the present stage, the research of the body area network mainly focuses on the system architecture and node energy consumption, the studies of security aspect of user privacy data is relatively few. This paper puts forward The data aggregation privacy protection algorithm of body area network based on data partitioning based on the above problem, this algorithm uses data partitioning strategy to deal with privacy data, and combines encryption theory to achieve security aggregation of user privacy data of the body area network security aggregation. The result not only improves effectively the privacy protection of the user data of body area network, but also reduces data redundancy and the traffic of the nodes.

A Secure Transmission Protocol for Wireless Body Sensor Networks

A wireless body sensor network (WBSN) is a typically wearable wireless network deployed on a user' body, which consists of biosensors and a local personal wireless hub, which we commonly call wireless body sensor network controller (WBSNC). The sensitive micro data (SMD) of WBSN users is collected by biosensors and forwarded to the WBSNC before it is delivered to the data process center (healthcare terminal or remote server) for further processing. Since SMD involves user's personal privacy, it is important to protect secure transmission of SMD. Therefore it is crucial to admit only legitimate biosensors and WBSNC into the network in a WBSN. Ensuring the safety of data transmission from each biosensor to WBSNC or between WBSNC and the data process center is essential because of the open feature in wireless channel. In this paper, we present a novel kind of cryptography and authentication method to assure a secure network admission and transmission in a WBSN based on ID. The procedures in this system to establish keys for each biosensor and WBSNC must offer high communication validity and as low energy consumption as possible. In addition, we also propose a new way to protect BSN secure access for biosensor and WBSNC in order to block interference from Pseudo Node.

Personalized privacy protection in social networks

Due to the popularity of social networks, many proposals have been proposed to protect the privacy of the networks. All these works assume that the attacks use the same background knowledge. However, in practice, different users have different privacy protect requirements. Thus, assuming the attacks with the same background knowledge does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advantage of differences of users' privacy requirements. In this paper, we introduce a framework which provides privacy preserving services based on the user's personal privacy requests. Specifically, we define three levels of protection requirements based on the gradually increasing attacker's background knowledge and combine the label generalization protection and the structure protection techniques (i.e. adding noise edge or nodes) together to satisfy different users' protection requirements. We verify the effectiveness of the framework through extensive experiments.