Preserving scheme for user’s confidential information in smart grid based on digital watermark and asymmetric encryption

As an essential part of the industrial Internet of Things(IoT)in power systems,the development of advanced metering infrastructure(AMI)facilitates services such as energy monitoring,load forecasting,and demand response.However,there is a growing risk of privacy disclosure with the wide installation of smart meters,for they transmit readings and sensitive data simultaneously.To guarantee the confidentiality of the sensitive information and authenticity of smart meter readings,we proposed a privacy-preserving scheme based on digital watermarking and elliptic-curve cryptography(ECC)asymmetric encryption.The sensitive data are encrypted using the public key and are hidden in the collected readings using digital watermark.Only the authorized user can extract watermark and can decrypt the confidential data using its private key.The proposed method realizes secure end-to-end confidentiality of the sensitive information.It has faster computing speed and can verify the data source and ensure the authenticity of readings.The example results show that the proposed method has little influence on the original data and unauthorized access cannot be completed within a reasonable time.On embedded hardware,the processing speed of the proposed method is better than the existing methods.

A Method of Eliminating Information Disclosure in View Publishing

Although it is convenient to exchange data by publishing view, but it may disclose sensitive information. The problem of how to eliminate information disclosure becomes a core problem in the view publishing process. In order to eliminate information disclosure, deciding view security algorithm and eliminating information disclosure algorithm are proposed, and the validity of the algorithms are proved by experiment. The experimental results showing, deciding view security algorithm can decide the safety of a set of views under prior knowledge, and eliminating information disclosure algorithm can eliminate disclosure efficiently.

Content sensitivity based access control framework for Hadoop

Big data technologies have seen tremendous growth in recent years. They are widely used in both industry and academia. In spite of such exponential growth, these technologies lack adequate measures to protect data from misnse/abuse. Corporations that collect data from multiple sources are at risk of liabilities due to the exposure of sensitive information. In the current implementation of Hadoop, only file-level access control is feasible. Providing users with the ability to access data based on the attlibutes in a dataset or the user＇s role is complicated because of the sheer volume and multiple formats （structured, unstructured and semi-structured） of data. In this paper, we propose an access control framework, which enforces access control policies dynamically based on the sensitivity of the data. This framework enforces access control policies by harnessing the data context, usage patterns and informat/on sensitivity. Information sensitivity changes over time with the addition and removal of datasets, which can lead to modifications in access control decisions. The proposed framework accommodates these changes. The proposed framework is automated to a large extent as the data itself determines the sensitivity with minimal user intervention. Our experimental results show that the proposed framework is capable of enforcing access control policies on non-multimedia datasets with minimal overhead.

Sensitive Information Protection Model Based on Bayesian Game

A game measurement model considering the attacker’s knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of service.We quantified the sensitive level of information according to the user’s personalized sensitive information protection needs.Based on the probability distribution of sensitive level and attacker’s knowledge background type,the strategy combination of service provider and attacker was analyzed,and a game-based sensitive information protection model was constructed.Through the combination of strategies under Bayesian equilibrium,the information entropy was used to measure the leakage of sensitive information.Furthermore,in the paper the influence of the sensitive level of information and the attacker’s knowledge background on the strategy of both sides of the game was considered comprehensively.Further on,the leakage of the user’s sensitive information was measured.Finally,the feasibility of the model was described by experiments.

Research on Fine-Grained Recognition Method for Sensitive Information in Social Networks Based on CLIP

With the emergence and development of social networks,people can stay in touch with friends,family,and colleagues more quickly and conveniently,regardless of their location.This ubiquitous digital internet environment has also led to large-scale disclosure of personal privacy.Due to the complexity and subtlety of sensitive information,traditional sensitive information identification technologies cannot thoroughly address the characteristics of each piece of data,thus weakening the deep connections between text and images.In this context,this paper adopts the CLIP model as a modality discriminator.By using comparative learning between sensitive image descriptions and images,the similarity between the images and the sensitive descriptions is obtained to determine whether the images contain sensitive information.This provides the basis for identifying sensitive information using different modalities.Specifically,if the original data does not contain sensitive information,only single-modality text-sensitive information identification is performed;if the original data contains sensitive information,multimodality sensitive information identification is conducted.This approach allows for differentiated processing of each piece of data,thereby achieving more accurate sensitive information identification.The aforementioned modality discriminator can address the limitations of existing sensitive information identification technologies,making the identification of sensitive information from the original data more appropriate and precise.

Protecting personalized privacy against sensitivity homogeneity attacks over road networks in mobile services

Privacy preservation has recently received considerable attention for location-based mobile services. A lot of location cloaking approaches focus on identity and location protection, but few algorithms pay attention to prevent sensitive information disclosure using query semantics. In terms of personalized privacy requirements, all queries in a cloaking set, from some user＇s point of view, are sensitive. These users regard the privacy is breached. This attack is called as the sensitivity homogeneity attack. We show that none of the existing location cloaking approaches can effectively resolve this problem over road networks. We propose a （K, L, P）-anonymity model and a personalized privacy protection cloaking algorithm over road networks, aiming at protecting the identity, location and sensitive information for each user. The main idea of our method is first to partition users into different groups as anonymity requirements. Then, unsafe groups are adjusted by inserting relaxed conservative users considering sensitivity requirements. Finally, segments covered by each group are published to protect location information. The efficiency and effectiveness of the method are validated by a series of carefully designed experiments. The experimental results also show that the price paid for defending against sensitivity homogeneity attacks is small.

Resolving conflicts between negotiation success and sensitive information protection in automated trust negotiation

Automated trust negotiation （ATN） is an approach to establishing mutual trust between strangers wishing to share resources or conduct business by gradually requesting and disclosing digitally signed credentials. In ATN, there are conflicts between negotiation success and sensitive information protection, that is, these two needs cannot be given priority at the same time, which is a challenging problem to resolve. In this paper, a language independent ATN framework, which is dynamic, flexible and adaptive, is presented to address this problem, ensuring negotiation success without sensitive information leakage. This framework is independent of the policy language which is used. However, the language used should have the capability to specify all kinds of sensitive information appearing in credentials and policies, and support the separation of attribute disclosure from credential disclosure. Thus definitions of new language features, which can be incorporated into existing policy languages, are given, enabling the used language to support the capabilities mentioned above.