In cloud computing, there have led to an increase in the capability to store and record personal data (microdata) in the cloud. In most cases, data providers have no/little control that has led to concern that the personal data may be beached. Microaggregation techniques seek to protect microdata in such a way that data can be published and mined without providing any private information that can be linked to specific individuals. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a sorting framework for Statistical Disclosure Control (SDC) to protect microdata in cloud computing. It consists of two stages. In the first stage, an algorithm sorts all records in a data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage a microaggregation method is used to create k-anonymous clusters while minimizing the information loss. The performance of the proposed techniques is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithms perform significantly better than existing associate techniques in the literature.

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High efficiency and linearity can be achieved using Kahn's envelope elimination and restoration (EER) technique. However, when used with modulation schemes such as CDMA and orthogonal frequency division multiplexing (OFDM), the resulting polar drive signals suffers from expanded bandwidth. Rudolph has shown that the bandwidth of the polar drive signals can be reduced by hole punching (limiting the minimum envelop of the modulated signal). This paper quantifies the error vector magnitude (EVM) caused by the ensuing distortion and compares the performance to a distortionless method based on the well known partial transmit sequence (PTS) method for OFDM. Although the hole punch process performs better than the PTS approach in terms of spectrum improvement, its EVM will limit the data throughput of the transmission.