Security Economics: A Guide for Data Availability and Needs

The rapid and accelerating development of security economics has generated great demand for more and better data to accommodate the empirical research agenda. The present paper serves as a guide to policy makers and researchers for security-related databases. The paper focuses on two main issues. Firstly, it takes stock of the existing databases, highlighting their main components and also performs a brief statistical comparison. Secondly, it discusses data shortages and needs that are considered essential for enhancing our understanding of the complex phenomenon of terrorism as well as designing and evaluating policy.

Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation

The growing expanse of e-commerce and the widespread availability of online databases raise many fears regarding loss of privacy and many statistical challenges. Even with encryption and other nominal forms of protection for individual databases, we still need to protect against the violation of privacy through linkages across multiple databases. These issues parallel those that have arisen and received some attention in the context of homeland security. Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. We present an overview of some proposals that have surfaced for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literature on privacy-preserving data mining. In particular, we focus on the matching problem across databases and the concept of ``selective revelation'' and their confidentiality implications.Comment: Published at http://dx.doi.org/10.1214/088342306000000240 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Security Economics: A Guide for Data Availability and Needs

The rapid and accelerating development of security economics has generated great demand for more and better data to accommodate the empirical research agenda. The present paper serves as a guide to policy makers and researchers for security-related databases. The paper focuses on two main issues. Firstly, it takes stock of the existing databases, highlighting their main components and also performs a brief statistical comparison. Secondly, it discusses data shortages and needs that are considered essential for enhancing our understanding of the complex phenomenon of terrorism as well as designing and evaluating policy

On the relation between Differential Privacy and Quantitative Information Flow

Differential privacy is a notion that has emerged in the community of statistical databases, as a response to the problem of protecting the privacy of the database's participants when performing statistical queries. The idea is that a randomized query satisfies differential privacy if the likelihood of obtaining a certain answer for a database $x$ is not too different from the likelihood of obtaining the same answer on adjacent databases, i.e. databases which differ from $x$ for only one individual. Information flow is an area of Security concerned with the problem of controlling the leakage of confidential information in programs and protocols. Nowadays, one of the most established approaches to quantify and to reason about leakage is based on the R\'enyi min entropy version of information theory. In this paper, we analyze critically the notion of differential privacy in light of the conceptual framework provided by the R\'enyi min information theory. We show that there is a close relation between differential privacy and leakage, due to the graph symmetries induced by the adjacency relation. Furthermore, we consider the utility of the randomized answer, which measures its expected degree of accuracy. We focus on certain kinds of utility functions called "binary", which have a close correspondence with the R\'enyi min mutual information. Again, it turns out that there can be a tight correspondence between differential privacy and utility, depending on the symmetries induced by the adjacency relation and by the query. Depending on these symmetries we can also build an optimal-utility randomization mechanism while preserving the required level of differential privacy. Our main contribution is a study of the kind of structures that can be induced by the adjacency relation and the query, and how to use them to derive bounds on the leakage and achieve the optimal utility

How (Not) to Index Order Revealing Encrypted Databases

Order Reveling Encryption (ORE) enables efficient range queries on encrypted databases, but may leak information that could be exploited by inference attacks. State-of-the-art ORE schemes claim different security guarantees depending on the adversary attack surface. Intuitively, online adversaries who access the database server at runtime may access information leakage; offline adversaries who access only a snapshot of the database data should not be able to gain useful information. We focus on offline security of the ORE scheme proposed by Lewi and Wu (LW-ORE, CCS 2016), which guarantees semantic security of ciphertexts stored in the database, but requires that ciphertexts are maintained sorted with regard to the corresponding plaintexts to support sublinear time queries. The design of LW-ORE does not discuss how to build indexing data structures to maintain sorting. The risk is that practitioners consider indexes as a technicality whose design does not affect security. We show that indexes can affect offline security of LW-ORE because they may leak duplicate plaintext values, and statistical information on plaintexts distribution and on transactions history. As a real-world demonstration, we found two open source implementations related to academic research (JISA 2018, VLDB 2019), and both adopt standard search trees which may introduce such vulnerabilities. We discuss necessary conditions for indexing data structures to be secure for ORE databases, and we outline practical solutions. Our analyses could represent an insightful lesson in the context of security failures due to gaps between theoretical modeling and actual implementation, and may also apply to other cryptographic techniques for securing outsourced databases

A Brief Bibliometric Analysis and Visualisation of Scopus and WoS databases on Blockchain Technology in Healthcare Domain

Background: The aim of this study is to analyse the work carried out in healthcare or medical domain using blockchain technology for privacy and security of patient’s data, their healthcare records. The documents published in Scopus and Web of Science databases during the year 2016 to present (February 2021) have been considered for survey. Methods: Scopus and Web of Science(WoS), most popular databases are used to retrieve documents which were published between years 2016 to present. Scopus analyser and web of Science analyser are used for analysis of various parameters such as documents published per year, sources of documents, number of citations and so on. VOSviewer1.6.16 software tool is used for analysis of different units such as citations, co- authorship etc. Results: During our survey we have retrieved a total 598 documents related to blockchain technology in the healthcare domain which are published from year 2016 on wards from scopus database. Using a web of science database 594 documents has been retrieved for the same domain. Statistical analysis and network analysis shows that there is tremendous growth in publications from year 2019 and 2020 on blockchain technology. The United States, India and China are major contributors. Conclusions: Databases are analysed in terms of number of documents per year, sources of publications, authors correlation, documents per country, funding agencies etc parameters are statistically analysed. Using statistical and network analysis we can conclude that there is huge scope to work in the blockchain domain to achieve more privacy, security, and data integrity

Mathematical techniques for the protection of patient's privacy in medical databases

In modern society, keeping the balance between privacy and public access to information is becoming a widespread problem more and more often. Valid data is crucial for many kinds of research, but the public good should not be achieved at the expense of individuals. While creating a central database of patients, the CSIOZ wishes to provide statistical information for selected institutions. However, there are some plans to extend the access by providing the statistics to researchers or even to citizens. This might pose a significant risk of disclosure of some private, sensitive information about individuals. This report proposes some methods to prevent data leaks. One category of suggestions is based on the idea of modifying statistics, so that they would maintain importance for statisticians and at the same time guarantee the protection of patient's privacy. Another group of proposed mechanisms, though sometimes difficult to implement, enables one to obtain precise statistics, while restricting such queries which might reveal sensitive information

Quantifying Privacy: A Novel Entropy-Based Measure of Disclosure Risk

It is well recognised that data mining and statistical analysis pose a serious treat to privacy. This is true for financial, medical, criminal and marketing research. Numerous techniques have been proposed to protect privacy, including restriction and data modification. Recently proposed privacy models such as differential privacy and k-anonymity received a lot of attention and for the latter there are now several improvements of the original scheme, each removing some security shortcomings of the previous one. However, the challenge lies in evaluating and comparing privacy provided by various techniques. In this paper we propose a novel entropy based security measure that can be applied to any generalisation, restriction or data modification technique. We use our measure to empirically evaluate and compare a few popular methods, namely query restriction, sampling and noise addition.Comment: 20 pages, 4 figure