The architectural, engineering and construction (AEC) requires most efficient joint efforts between the construction project stakeholders with continuing exchange of large amount of project data. Nowadays, it is seen paradigm shift in construction industry to work on digital technologies like BIM from conventional methods of paper-based data exchange. The expanding volume of individual and sensitive information being digitally gathered by the AEC industry and store in the cloud, which makes it vulnerable to cyber attack. According to the Data Protection Act and the General Data Protection Regulation, organisation has to ensure security of people and security of sensitive data. Subsequently, it is crucial to establish the framework or method to provide the privacy of project data. Specifically, individual information, for example, individual health records, address and all other background information should be protected as per the legal regulation In this research paper, a technique called Hybrid-k anonymity has been proposed to protect the individual’s personal information as well as employees details, supplier details, cost details. In this technique, we modified original data using randomisation technique and then apply anonymisation on modified data which can provide better accuracy with minimum loss of information. This approach will enhance the privacy of sensitive information from cyber attack of the data miner.

Data privacy in construction industry by privacy-preserving data mining (PPDM) approach

A large number of problems in business and consumer analytics have input graphs or networks. These mathematical entities have a long standing tradition in discrete applied mathematics and computer science. In many cases, they are the most natural means to represent some type of relationships in data. Consequently, a large number of solution methods based on heuristics and exact algorithms exist for problems that have graphs and/or networks as part of their input. While the number of possible applications of these techniques is not limited to problems in business and customer analytics, we have chosen to present some of them in a survey that would allow newcomers to the field of data science to create some familiarity with the key questions that motivate the area. We have also provided a survey on recent applications and new algorithmic approaches for data analytics. In addition we discuss issues related to the computational complexity of some problems associated with them. Other chapters of this section complement the discussion in this chapter with specific examples of interest or that could motivate new novel research direction and application.

Business Network Analytics: From Graphs to Supernetworks.

With the development of mobile devices which are integrated position sensors, users share their locations by checking in on geo-social network systems, such as Foursquare and Facebook Places. There are various purposes for the location-rich social network could be of research interest. However, such datasets may face the privacy concerns, like reidentification and privacy violation if they are not adequately anonymized. In this paper, we study the problem of anonamizing the geo-social network dataset, based on adversarial knowledge of location information of its users. We propose a novel approach for preserving privacy in geo-social network data publishing based on location contact graph, which considers the relationship of locations and “co-location” pattern. Based on this graph, we propose a graph similarity based anonymization (GSA) algorithm, a k-anonymity-based method to guarantee anonymity. We also evaluate the algorithm using two real datasets, Gowalla and Brightkite, and experiment results show that the performance of the method is convincing.

Contact Graph Based Anonymization for Geosocial Network Datasets

With the development of mobile technology, geo-social network (GSN) data is frequently collected and released for further analysis by data collectors in order to provide personalized service, while the released dataset may contain private and sensitive information. Even though some data collectors apply a number of simple anonymization-based techniques, sensitive information of users and locations may be still disclosed with high possibility because of the variety of heterogeneous background knowledge an adversary obtains from the published GSN. Therefore, privacy preserving is a bottleneck in GSN publishing. It is well known that data collected from real world often has multiparty relationships and be presented by hypergraph. While the traditional graph-differential privacy (DP) is not suitable for providing privacy to hypergraph because it can not capture the heterogeneous background knowledge attack and is deficient to provide privacy for high-order relationship in hypergraph-represented data. Recently, some studies dased on differential privacy [1,2] and encryption mechanism [3–5] attempted to provide privacy preserving to GSN data publication, yet facing challenges in reducing noise injection. In this paper, as inspired by the works of Li et al. [6], we first propose a differential privacy model for hypergraph-represented GSN publication, which is able to provide strong privacy for the multi-party relationship in GSN. We develop the notion of differential privacy from graph data to hypergraph data and achieve differential privacy in both location and social

Geo-social network publication based on differential privacy

Social networks are often published in the form of a simple graph. The simple graph representation of a social graph shows the dyadic relationship among the social entities whereas it is unable to efficiently represent the relationship among more than two entities, such as the relationship found in the social groups. This type of relationship is called super-dyadic relationship, and it can be effectively represented by a hypergraph model. This work proposes an anonymization scheme called rank-label anonymization for the privacy-preserving publication of a hypergraph structure. Here, an attack model called rank-label attack is proposed, and an anonymization solution is provided to counter this attack. The percentage of disclosure risk shows that the rank-label attack is stronger than the existing rank attack. We propose a method based on sequential clustering to achieve rank-label anonymization called sequential rank-label anonymization (SA). Another algorithm called greedy rank-label anonymization (GA) is also proposed. The quality of the anonymization solution reported by SA and GA is compared with the help of normalized anonymization cost ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NCost ). Results show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NCost reported by SA is less than that of GA for both Adult and MAG-10 datasets. In Adult dataset, approximately 58% and 62% reduction in the average execution time of GA and SA are obtained than that of a general-purpose computing system due to the use of a high-performance computing system. In MAG-10 dataset, this average reduction percentage is reported to be 56% for GA and 53% for SA. The time complexity of SA is found to be O(n 4 ) whereas it is O(n 3 ) in case of GA.

Rank-Label Anonymization for the Privacy-Preserving Publication of a Hypergraph Structure

Protecting private geosocial networks against practical hybrid attacks with heterogeneous information

Cloud computing is becoming increasingly popular due to its power in providing high-performance and flexible service capabilities. More and more internet users have accepted this innovative service model and been using various cloud-based services every day. However, these service-using data is quite valuable for marketing purposes, as it can reflect a user's interest and service-using pattern. Therefore, the privacy issues have been brought out. Recently, many studies focus on access control and other traditional security problems in cloud, and little studied on the topic of the private service data publishing. In this paper, we study the private service data publishing problem by representing the data with a hyper graph, which is quite efficient to illustrate complex relationships among users. We first formulate the problem with a popular background knowledge attack model named rank attack, and then provide an anonymization-based method to prevent the released data from such attacks. We also take data utility into consideration by defining specific information loss metrics. The performances of the methods have been validated by two sets of synthetic data.

Preserving Private Cloud Service Data Based on Hypergraph Anonymization

GeoSocial Networks (GSNs) are becoming increasingly popular due to its power in providing high-performance and flexible service capabilities. More and more internet users have accepted this innovative service model. However, although GSNs have great business value for data analysis by integrated with location information, it may seriously compromise users' privacy in publishing the GSN data. In this paper, we study the identity disclosure problem in publishing GSN data. We first discuss the attack problem by considering location-based properties as background knowledge, and then formalize an attack model, named (k, m)-anonymity. Then we propose a complete solution to achieve (k, m)-anonymization to prevent the released data from the attack. We also take data utility into consideration by defining specific information loss metrics. The performances of the methods have been validated by the real-world data.

Yuechuan Li

Papers

Protecting private geosocial networks against practical hybrid attacks with heterogeneous information

Preserving Private Cloud Service Data Based on Hypergraph Anonymization

On Preserving Private Geosocial Networks against Practical Attacks