Social security data : an aid to manpower evaluation /

"November 1970."

Linkages with data from Social Security administrative records in the Health and Retirement Study /

"August 1999."

Income security for persons with limited income : program summaries, recipient and expenditure data /

"77-152 ED. HV84A."

Earnings and employment data for wage and salary workers covered under social security by state and county /

Description based on: 1984.

Earnings and employment data for workers covered under social security, by state and county /

Mode of access: Internet.

Considerations in the selection of security measures for automatic data processing systems /

"Contributed to the Federal Information Processing Standards Task Group 15 - Computer Systems Security" -t.p.

How does social security affect economic growth? Evidence from cross-country data

This paper investigates how social security interacts with growth and growth determinants (savings, human capital investment, and fertility). Our empirical investigation finds that the estimated coefficient on social security is significantly negative in the fertility equation, insignificant in the saving equation, and significantly positive in the growth and education equations. By contrast, the estimated coefficient on growth is insignificant in the social security equation. The results suggest that social security may indeed be conducive to growth through tipping the trade-off between the number and quality of children toward the latter.

Big data storage security

The demand for data storage and processing is increasing at a rapid speed in the big data era. The management of such tremendous volume of data is a critical challenge to the data storage systems. Firstly, since 60% of the stored data is claimed to be redundant, data deduplication technology becomes an attractive solution to save storage space and traffic in a big data environment. Secondly, the security issues, such as confidentiality, integrity and privacy of the big data should also be considered for big data storage. To address these problems, convergent encryption is widely used to secure data deduplication for big data storage. Nonetheless, there still exist some other security issues, such as proof of ownership, key management and so on. In this chapter, we first introduce some major cyber attacks for big data storage. Then, we describe the existing fundamental security techniques, whose integration is essential for preventing data from existing and future security attacks. By discussing some interesting open problems, we finally expect to trigger more research efforts in this new research field.

Security of Two-Party Identity-Based Key Agreement

Identity-based cryptography has become extremely fashionable in the last few years. As a consequence many proposals for identity-based key establishment have emerged, the majority in the two party case. We survey the currently proposed protocols of this type, examining their security and efficiency. Problems with some published protocols are noted.

Extending the advanced forensic format to accommodate multiple data sources, logical evidence, arbitrary information and forensic workflow

Forensic analysis requires the acquisition and management of many different types of evidence, including individual disk drives, RAID sets, network packets, memory images, and extracted files. Often the same evidence is reviewed by several different tools or examiners in different locations. We propose a backwards-compatible redesign of the Advanced Forensic Formatdan open, extensible file format for storing and sharing of evidence, arbitrary case related information and analysis results among different tools. The new specification, termed AFF4, is designed to be simple to implement, built upon the well supported ZIP file format specification. Furthermore, the AFF4 implementation has downward comparability with existing AFF files.

A medical data reliability assessment model

There is currently a strong focus worldwide on the potential of large-scale Electronic Health Record (EHR) systems to cut costs and improve patient outcomes through increased efficiency. This is accomplished by aggregating medical data from isolated Electronic Medical Record databases maintained by different healthcare providers. Concerns about the privacy and reliability of Electronic Health Records are crucial to healthcare service consumers. Traditional security mechanisms are designed to satisfy confidentiality, integrity, and availability requirements, but they fail to provide a measurement tool for data reliability from a data entry perspective. In this paper, we introduce a Medical Data Reliability Assessment (MDRA) service model to assess the reliability of medical data by evaluating the trustworthiness of its sources, usually the healthcare provider which created the data and the medical practitioner who diagnosed the patient and authorised entry of this data into the patient’s medical record. The result is then expressed by manipulating health record metadata to alert medical practitioners relying on the information to possible reliability problems.

A time-variant medical data trustworthiness assessment model

Electronic Health Record (EHR) systems are being introduced to overcome the limitations associated with paper-based and isolated Electronic Medical Record (EMR) systems. This is accomplished by aggregating medical data and consolidating them in one digital repository. Though an EHR system provides obvious functional benefits, there is a growing concern about the privacy and reliability (trustworthiness) of Electronic Health Records. Security requirements such as confidentiality, integrity, and availability can be satisfied by traditional hard security mechanisms. However, measuring data trustworthiness from the perspective of data entry is an issue that cannot be solved with traditional mechanisms, especially since degrees of trust change over time. In this paper, we introduce a Time-variant Medical Data Trustworthiness (TMDT) assessment model to evaluate the trustworthiness of medical data by evaluating the trustworthiness of its sources, namely the healthcare organisation where the data was created and the medical practitioner who diagnosed the patient and authorised entry of this data into the patient’s medical record, with respect to a certain period of time. The result can then be used by the EHR system to manipulate health record metadata to alert medical practitioners relying on the information to possible reliability problems.