A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA

In this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone's life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone's personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exists for tabular data but you can't apply directly to the structured complex graph  data,which consists of vertices represented as individuals and edges representing some kind of connection or relationship between the nodes. Various techniques like K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes and techniques like edge perturbation, edge randomization are there to provide privacy to edges in social graphs. Development of new techniques by  Integration to exiting techniques like K-anonymity ,edge perturbation, edge randomization, L-Diversity are still going on to provide more privacy to relational data and social network data are ongoingin the best possible manner.Â

Conservative interventions for urinary incontinence in women: an overview of Cochrane systematic reviews

This is the protocol for a review and there is no abstract. The objectives are as follows:  To synthesise Cochrane reviews of conservative interventions, as described above, for the prevention or treatment of female urinary incontinence.Output Type: Protoco

‘Enhanced Encryption and Fine-Grained Authorization for Database Systems

The aim of this research is to enhance fine-grained authorization and encryption so that database systems are equipped with the controls necessary to help enterprises adhere to zero-trust security more effectively. For fine-grained authorization, this thesis has extended database systems with three new concepts: Row permissions, column masks and trusted contexts. Row permissions and column masks provide data-centric security so the security policy cannot be bypassed as with database views, for example. They also coexist in harmony with the rest of the database core tenets so that enterprises are not forced to compromise neither security nor database functionality. Trusted contexts provide applications in multitiered environments with a secure and controlled manner to propagate user identities to the database and therefore enable such applications to delegate the security policy to the database system where it is enforced more effectively. Trusted contexts also protect against application bypass so the application credentials cannot be abused to make database changes outside the scope of the application’s business logic. For encryption, this thesis has introduced a holistic database encryption solution to address the limitations of traditional database encryption methods. It too coexists in harmony with the rest of the database core tenets so that enterprises are not forced to choose between security and performance as with column encryption, for example. Lastly, row permissions, column masks, trusted contexts and holistic database encryption have all been implemented IBM DB2, where they are relied upon by thousands of organizations from around the world to protect critical data and adhere to zero-trust security more effectively

Data Exfiltration:A Review of External Attack Vectors and Countermeasures

AbstractContext One of the main targets of cyber-attacks is data exfiltration, which is the leakage of sensitive or private data to an unauthorized entity. Data exfiltration can be perpetrated by an outsider or an insider of an organization. Given the increasing number of data exfiltration incidents, a large number of data exfiltration countermeasures have been developed. These countermeasures aim to detect, prevent, or investigate exfiltration of sensitive or private data. With the growing interest in data exfiltration, it is important to review data exfiltration attack vectors and countermeasures to support future research in this field. Objective This paper is aimed at identifying and critically analysing data exfiltration attack vectors and countermeasures for reporting the status of the art and determining gaps for future research. Method We have followed a structured process for selecting 108 papers from seven publication databases. Thematic analysis method has been applied to analyse the extracted data from the reviewed papers. Results We have developed a classification of (1) data exfiltration attack vectors used by external attackers and (2) the countermeasures in the face of external attacks. We have mapped the countermeasures to attack vectors. Furthermore, we have explored the applicability of various countermeasures for different states of data (i.e., in use, in transit, or at rest). Conclusion This review has revealed that (a) most of the state of the art is focussed on preventive and detective countermeasures and significant research is required on developing investigative countermeasures that are equally important; (b) Several data exfiltration countermeasures are not able to respond in real-time, which specifies that research efforts need to be invested to enable them to respond in real-time (c) A number of data exfiltration countermeasures do not take privacy and ethical concerns into consideration, which may become an obstacle in their full adoption (d) Existing research is primarily focussed on protecting data in ‘in use’ state, therefore, future research needs to be directed towards securing data in ‘in rest’ and ‘in transit’ states (e) There is no standard or framework for evaluation of data exfiltration countermeasures. We assert the need for developing such an evaluation framework

Insider Misuse Identification using Transparent Biometrics

Insider misuse is a key threat to organizations. Recent research has focused upon the information itself – either through its protection or approaches to detect the leakage. This paper seeks a different approach through the application of transparent biometrics to provide a robust approach to the identification of the individuals who are misusing systems and information. Transparent biometrics are a suite of modalities, typically behavioral-based that can capture biometric signals covertly or non-intrusively – so the user is unaware of their capture. Transparent biometrics are utilized in two phases a) to imprint digital objects with biometric-signatures of the user who last interacted with the object and b) uniquely applied to network traffic in order to identify users traffic (independent of the Internet Protocol address) so that users rather than machine (IP) traffic can be more usefully analyzed by analysts. Results from two experimental studies are presented and illustrate how reliably transparent biometrics are in providing this link-ability of information to identity.

On the security of NoSQL cloud database services

Processing a vast volume of data generated by web, mobile and Internet-enabled devices, necessitates a scalable and flexible data management system. Database-as-a-Service (DBaaS) is a new cloud computing paradigm, promising a cost-effective and scalable, fully-managed database functionality meeting the requirements of online data processing. Although DBaaS offers many benefits it also introduces new threats and vulnerabilities. While many traditional data processing threats remain, DBaaS introduces new challenges such as confidentiality violation and information leakage in the presence of privileged malicious insiders and adds new dimension to the data security. We address the problem of building a secure DBaaS for a public cloud infrastructure where, the Cloud Service Provider (CSP) is not completely trusted by the data owner. We present a high level description of several architectures combining modern cryptographic primitives for achieving this goal. A novel searchable security scheme is proposed to leverage secure query processing in presence of a malicious cloud insider without disclosing sensitive information. A holistic database security scheme comprised of data confidentiality and information leakage prevention is proposed in this dissertation. The main contributions of our work are: (i) A searchable security scheme for non-relational databases of the cloud DBaaS; (ii) Leakage minimization in the untrusted cloud. The analysis of experiments that employ a set of established cryptographic techniques to protect databases and minimize information leakage, proves that the performance of the proposed solution is bounded by communication cost rather than by the cryptographic computational effort