An attack-resistant hybrid data-privatization method with low information loss

We examine a recent proposal for data-privatization by testing it against well-known attacks, we show that all of these attacks successfully retrieve a relatively large (and unacceptable) portion of the original data. We then indicate how the data-privatization method examined can be modified to assist it to withstand these attacks and compare the performance of the two approaches. We also show that the new method has better privacy and lower information loss than the former method.

Recovering private data : a comparison of three methods

We examine a recent proposal for data privatization by testing it agalnst three well-known attacks. We show that all three attacks successfully retrieve the original datta. We compare the strengths of the three attacks. Finally, we indicate how the data privatization method examined can be modified to assist it to withstand these attacks.

Privatization and economic growth in Australia: the shorthand of a long process

The impact of privatization on economic growth has been little investigated relative to disaggregated approaches. A growth accounting framework is used here to investigate the impact of privatization on growth for the  Australian economy. The contribution of public capital to the private sector and whether the growth process is endogenous or Solow is evaluated. Separate measures of public and private capital are computed in order to estimate their impacts with labour on Australian gross domestic product (GDP) growth for the period 1960 to 2003. A simple growth rates version is found preferred by stationarity and other tests. Labour growth appears to strongly positively influence the growth of GDP. In contrast, public capital growth has no statistically significant effect on GDP growth, or on private capital productivity. The data are consistent with the hypothesis that the coefficients of the growth equation are the same before and during privatization.

Aggregating privatized medical data for secure querying applications

 This thesis analyses and examines the challenges of aggregation of sensitive data and data querying on aggregated data at cloud server. This thesis also delineates applications of aggregation of sensitive medical data in several application scenarios, and tests privatization techniques to assist in improving the strength of privacy and utility.