Towards publishing directed social network data with k‐degree anonymization

Existing works on identity privacy protection on social networks make the assumption that all the user identities in a social network are private and ignore the fact that in many real-world social networks, there exists a considerable amount of users such as celebrities, media users, and organization users whose identities are public. In this paper, we demonstrate that the presence of public users can cause serious damage to the identity privacy of other ordinary users. Motivated attackers can utilize the connection information of a user to some known public users to perform re-identification attacks, namely connection fingerprint (CFP) attacks. We propose two k-anonymization algorithms to protect a social network against the CFP attacks. One algorithm is based on adding dummy vertices. It can resist powerful attackers with the connection information of a user with the public users within n hops (n ≥ 1) and protect the centrality utility of public users. The other algorithm is based on edge modification. It is only able to resist attackers with the connection information of a user with the public users within 1 hop but preserves a rich spectrum of network utility. We perform comprehensive experiments on real-world networks and demonstrate that our algorithms are very efficient in terms of the running time and are able to generate k-anonymized networks with good utility.

3GPP Release 15, the first 5G standard, includes protection of user identity privacy against IMSI catchers. These protection mechanisms are based on public key encryption. Despite this protection, IMSI catching is still possible in LTE networks which opens the possibility of a downgrade attack on user identity privacy, where a fake LTE base station obtains the identity of a 5G user equipment. We propose (i) to use an existing pseudonym-based solution to protect user identity privacy of 5G user equipment against IMSI catchers in LTE and (ii) to include a mechanism for updating LTE pseudonyms in the public key encryption based 5G identity privacy procedure. The latter helps to recover from a loss of synchronization of LTE pseudonyms. Using this mechanism, pseudonyms in the user equipment and home network are automatically synchronized when the user equipment connects to 5G. Our mechanisms utilize existing LTE and 3GPP Release 15 messages and require modifications only in the user equipment and home network in order to provide identity privacy. Additionally, lawful interception requires minor patching in the serving network.

Recently, there is an increase interest in location sharing services in social networks. Behind the convenience brought by location sharing, there comes an indispensable security risk of privacy. Though many efforts have been made to protect user’s privacy for location sharing, they are not suitable for social network. Most importantly, little research so far can support user relationship privacy and identity privacy. Thus, we propose a new privacy protection protocol for location sharing in social networks. Different from previous work, the proposed protocol can provide perfect privacy for location sharing services. Simulation results validate the feasibility and efficiency of the proposed protocol.

In the era of 5G, billions of terminal devices achieve global interconnection and intercommunication, which leads to the generation of massive data. However, the existing cloud‐based data‐sharing mechanism faces challenges such as sensitive information leakage and data islands, which makes it difficult to achieve secure sharing across domains. In this paper, the authors propose a fine‐grained data‐sharing scheme based on blockchain and ciphertext policy attribute‐based encryption, and design a verifiable outsourced computation method to reduce the computational pressure of end users. Second, the authors comprehensively consider the user's identity privacy and transaction privacy, and propose a multi‐level privacy protection method based on ring signature and garbled bloom filter, which enhance the user's data privacy and availability, and prevent the traceability of requests. Finally, the authors design a set of interconnected smart contracts, and verify that their scheme can achieve secure and efficient data sharing through security analysis and performance testing.

A distributed access control module in wireless sensor networks (WSNs) allows the network to authorize and grant user access privileges for in-network data access. Prior research mainly focuses on designing such access control modules for WSNs, but little attention has been paid to protect user's identity privacy when a user is verified by the network for data accesses. Often, a user does not want the WSN to associate his identity to the data he requests, particularly in a single-owner multi-user WSN. In this paper, we present the design, implementation, and evaluation of a novel approach, Priccess, to ensure privacy-preserving access control. In addition to the theoretical analysis that demonstrates the security properties of Priccess, this paper also reports the experimental results of Priccess in a network of Imote2 motes, which show the efficiency of Priccess in practice.

Graph anonymization is a necessary process before any publication of data from a social network in order to preserve the privacy of individuals, especially when these networks contain sensitive or personally identifiable information, such as social contacts, personal opinions and recordings of private communications. So, this publication can therefore constitute a threat to privacy. Generally, this simple procedure is not sufficient, some inference attacks on the published graph can lead to de-anonymize certain nodes, to learn the existence of a social relation between two nodes or to use the structure of the graph itself to deduce the value of certain sensitive attributes. For this reason, several anonymization techniques were proposed in the literature to combat this problem, they modify the structure of the social graph by adding and/or removing nodes and/or edges, these methods can be classified as Graph Modification, Generalization or Clustering and Differential Privacy Models. In this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social network data. We identify the best graph anonymization method that ensure a high confidentiality level after making a clear comparison between them. Finally, we present our perspective or our proposition that still under development which is based on differential privacy method and attacking the graph processing in different ways at the same time, graph structure and node attributes.

Based on state projection method with two-step operations, this paper deals with the model reduction problem by analyzing the information descriptions of system states. Our basic idea in obtaining the reduced-order models is to minimize the information loss or the conditional information loss caused by truncation by eliminating the state variables with the least contribution to system information. Before truncation, an entropy preserving transformation of the original state is required. The derived minimum information loss (MIL) and minimum conditional information loss (MCIL) methods are proved to be efficient for approximating stable and unstable systems, respectively, and connected with the balanced truncation methods firmly. Illustrative examples are given.

The growing need to address privacy concerns when social network data is released for mining purposes has recently led to considerable interest in various techniques for graph anonymization. In this paper, we study the following problem: Given a social network modeled as an edge-labeled graph G, we aim to make a pre-specifled subset of vertices of G k-label sequence anonymous with the minimum number of edge additions. Here, the label sequence of a vertex is the sequence of labels of edges incident to it. The contributions of this paper are two fold: We provide a framework to show hardness results for different variants of social network anonymization using a common approach. We start by showing that k-label sequence anonymity of arbitrary labeled graphs is hard, and use this result to prove NP-hardness results for many other recently proposed notions of graph anonymization. Secondly, we present interesting algorithms and hardness for bipartite graphs. For unlabeled bipartite graphs, we show k-degree anonymity is in P for all k ≥ 2. For labeled bipartite graphs, we show that k-label sequence anonymity is in P for k = 2 but it is NP-hard for k ≥ 3.

Data anonymity, as an effective privacy protection method, has been widely used in real applications. High performance data anonymity algorithm is especially attractive for those massive data applications. In this paper, the authors propose a novel and efficient Datafly based data anonymity (Divide-Datafly) algorithm and the experimental results show that the proposed algorithm is not only more efficient than Datafly and Incognito, but also information loss less than KACA. Moreover, in order to improve the security of anonymous data, L-Divide-Datafly is presented that it combines Divide-Datafly and efficient distance based clustering. Experimental results show that L-Divide-Datafly achieves great performance both in execution time and Information loss.

To make various statistics about any medical data huge amount of data gets collected from the public. The collected information contains sensitive information about the individual. Individual's sensitive information includes data such as previous health condition, travel history, political affinity and family background. The collected data is published to perform various statistics, carry out research and to make decisions. However, there might be an intruder in the crowd who uses the published data to gain more information about a particular person that leads to privacy threat. Privacy Preserving Data Publishing (PPDP) aims at anonymizing such data so that it becomes difficult for an intruder to infer a various attribute of particular person from the published data and to gain more information about a him. When the data is anonymized, there are chances of data loss, hence the anonymization algorithm, must balance between the anonymity and the utility of the published data. Most of the existing privacy algorithm concentrate on preserving privacy of single sensitive attributes, but many domains require anonymizing multiple sensitive attributes such as healthcare/ hospital dataset, census dataset etc., that need to be preserved. In this paper an effective algorithm is proposed that preserves the privacy of multiple sensitive attributes with minimal information loss.

Now a day’s Data publishing process is the compulsory for visualizing the data sets to other parties. But in publishing process there is thread for data set owners by disclosing the sensitive information. For avoiding those problems we using anonymization techniques for secure data publishing. Most Anonymization techniques such as Generalization and Bucketization are using now. But these anonymization techniques have some limitation. Generalization for k-anonymity losses considerable amount of information for high- dimensional data and Bucketization does not prevent membership disclosure, because bucketization publishes the QI values in their original forms, It requires a clear separation between QI’s and SA’s, but in many data sets, it is unclear which attributes are QI’s and which are SA’s and it breaks the attributes correlations between the QI’s and the SA’s. This paper introduces new anonymization technique slicing to overcome all the drawbacks of bucketization and generalization Keywords: - Sensitive information, High dimensional data, data anonymization, data publishing, data security, generalization and bucketization.

As social network data contain sensitive information, the need arises for privacy preservation of released social network data for which the most deployed technique is anonymisation. There are a number of anonymisation techniques which have been proposed for privacy preservation in social network data. Not all the anonymisation techniques have been able to provide the desired privacy as they ignore one or more critical parameters. The purpose of this paper is to propose the parameters for anonymisation of released social network data based on the cross-pollination of information related to anonymisation of social network data. Analysing the anonymisation techniques on these parameters would provide a clear understanding in a more realist dimension. These parameters would help the service provider of the social network for choosing the suitable anonymisation technique. These parameters would also help the future researchers to design or improve the anonymisation technique for privacy preservation in social network.

Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on k-anonymity algorithms on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution. Toward this objective, we explore the impact of the attributes of sparse distribution on data utility. Firstly, we propose a new utility metric that emphasizes network structure distortion and attribute value loss. Furthermore, we design and implement a differentiated k-anonymity l-diversity social network anonymity algorithm, which seeks to protect users’ privacy in social networks and increase the usability of the published anonymized data. Its key idea is that it divides a node into two child nodes and only anonymizes sensitive values to satisfy anonymity requirements. The evaluation results show that our method can effectively improve the data utility as compared to generalized anonymizing algorithms.

The use of Javanese vocabulary at SDN Petompon 01 Semarang was not optimal. This wasbecause children locked practice in speaking Javanese and also the use of learning media is stillminimal. Responding to this phenomenon, researchers developed smart card learning media basedon Three-Dimensional Animation Videos with the integration of local wisdom to fosterenthusiasm and increase the use of Javanese vocabulary in the school. The research was carriedout on 2 July - 24 August 2023 at SDN Pentompon 01 Semarang. This research used R&D(Research and Development) with a qualitative approach. The research population was allstudents in class IIIA at SDN Petompon 01 Semarang, totaling 16 students. Small group trialresearch sample testing was carried out in class IIIB using a purposive sampling technique bytaking 6 students based on class ranking, namely three students in the top rank, two students inthe middle rank, and one student in the bottom rank. Large group trials were carried out in ClassIIIA. The dependent variable of this research was increasing Javanese vocabulary, while theindependent variable is developing smart card learning media based on Three-DimensionalAnimation Videos with the integration of local wisdom. The data collection techniques used wereobservation, interviews, documentation, questionnaires and tests. The instruments used areobservation guidelines, interview guidelines, questionnaire sheets, and test questions. Productdata analysis techniques and initial data analysis consist of a normality test and homogeneity testas well as final data analysis consisting of the N-Gain Test and also the T-test. The researchresults showed that an average of 61 pretest results and posttest scores showed an increase in theaverage student score of 67.5333 with a percentage increase of 41% in the pretest results and 44%in the posttest results with details of 10 students who did not complete. and 6 students completedit so it can be concluded that three-dimensional animated video-based learning media is quitefeasible and effective for use in learning Javanese

The objective of this study was to review the relative merits and potential of 15 (fifteen) alternative waste forms being considered for the solidification and disposal of radioactive wastes. The relative merits of 4 (four) alternative pre-solidification processing approaches were also assessed in this study. A Peer Review Panel composed of 8 (eight) scientists and engineers representing independent, non-DOE laboratories from industry, government, and universities and the disciplines of materials science, ceramics, glass, metallurgy, and geology conducted the review. A numerical rating of alternative waste forms was arrived at individually by the panel members taking into consideration 9 (nine) scientific and 9 (nine) engineering parameters affecting the long term performance and production of waste forms. At a meeting on May 9, 1980, a group ranking for the alternative forms was achieved by averaging the individual scores and discussing the available data base. Three final ranking lists comparing: (A) Present Scientific Merits or Least Risk for Use Today; and (B) Research Priority; and (C) Present and Potential Engineering Practicality were prepared by the Panel. Each waste form in the lists is assigned a value of either (1) Top Rank, (2) Intermediate Rank, or (3) Bottom Rank. A discussion of the relative strengths and weaknesses of the alternative waste forms and recommendations for future program directions is presented in the body of the accompanying Peer Review Panel report.