A Certificateless One-Way Group Key Agreement Protocol for Point-to-Point Email Encryption

Over the years, email has evolved and grown to one of the most widely used form of communication between individuals and organizations. Nonetheless, the current information technology standards do not value the significance of email security in today\u27s technologically advanced world. Not until recently, email services such as Yahoo and Google started to encrypt emails for privacy protection. Despite that, the encrypted emails will be decrypted and stored in the email service provider\u27s servers as backup. If the server is hacked or compromised, it can lead to leakage and modification of one\u27s email. Therefore, there is a strong need for point-to-point (P2P) email encryption to protect email user\u27s privacy. P2P email encryption schemes strongly rely on the underlying Public Key Cryptosystems (PKC). The evolution of the public key cryptography from the traditional PKC to the Identity-based PKC (ID-PKC) and then to the Certificateless PKC (CL-PKC) provides a better and more suitable cryptosystem to implement P2P email encryption. Many current public-key based cryptographic protocols either suffer from the expensive public-key certificate infrastructure (in traditional PKC) or the key escrow problem (in ID-PKC). CL-PKC is a relatively new cryptosystem that was designed to overcome both problems. In this thesis, we present a CL-PKC group key agreement protocol, which is, as the author\u27s knowledge, the first one with all the following features in one protocol: (1) certificateless and thus there is no key escrow problem and no public key certificate infrastructure is required. (2) one-way group key agreement and thus no back-and-forth message exchange is required; (3) n-party group key agreement (not just 2- or 3-party); and (4) no secret channel is required for key distribution. With the above features, P2P email encryption can be implemented securely and efficiently. This thesis provides a security proof for the proposed protocol using ``proof by simulation\u27\u27. Efficiency analysis of the protocol is also presented in this thesis. In addition, we have implemented the prototypes (email encryption systems) in two different scenarios in this thesis

Improved Supervised Classification of Accelerometry Data to Distinguish Behaviors of Soaring Birds

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data

A Certificateless One-Way Group Key Agreement Protocol for End-to-End Email Encryption

Over the years, email has evolved into one of the most widely used communication channels for both individuals and organizations. However, despite near ubiquitous use in much of the world, current information technology standards do not place emphasis on email security. Not until recently, webmail services such as Yahoo\u27s mail and Google\u27s gmail started to encrypt emails for privacy protection. However, the encrypted emails will be decrypted and stored in the service provider\u27s servers. If the servers are malicious or compromised, all the stored emails can be read, copied and altered. Thus, there is a strong need for end-to-end (E2E) email encryption to protect email user\u27s privacy. In this paper, we present a certificateless one-way group key agreement protocol with the following features, which are suitable to implement E2E email encryption: (1) certificateless and thus there is no key escrow problem and no public key certificate infrastructure is required; (2) one-way group key agreement and thus no back-and-forth message exchange is required; and (3) n-party group key agreement (not just 2- or 3-party). This paper also provides a security proof for the proposed protocol using proof by simulation . Finally, efficiency analysis of the protocol is presented at the end of the paper

Developing Accessible P2P Email Encryption Based on CLOW-GKA

Secure email encryption is increasingly fundamental to successful personal and corporate communication. This is especially true as perpetual technological innovation makes assuring the integrity and authenticity of data more challenging. However current information technology standards places little emphasis on email encryption. CLOW-GKA (Certificateless One Way Group Key Agreement Scheme) is a new P2P (Point to Point) encryption system that eliminates the need for third party verification. The cryptosystem draws from such other schemes as Phil Zimmerman’s PGP (Pretty Good Privacy), Shamir’s ID-PKC (Identity Based Public Key Cryptosystem), and Al-Riyami’s CL-PKC (Certificateless PKC). This project’s purpose is developing an accessible graphical user-interface (GUI) that implements CLOW-GKA. In particular, we focus on devising a GUI that is compatible with Gmail services, performs with comparable functionality, and offers better security

Data Set for Improved Supervised Classification of Accelerometry Data to Distinguish Behaviors of Soaring Birds

The accelerometer data collected and uploaded here is for the publication titled Improved Supervised Classification of Accelerometry Data to Distinguish Behaviors of Soaring Birds . Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data

Flight behavior of 5 free-ranging golden eagles interpreted from acceleration data.

<p>Plots show percentage of time spent in flapping or soaring flight at different times of a day (a,b) and flight behavior as a function of flight altitude (c,d). Behavior was classified with (a, c) a random forest model and (b, d) a K-nearest neighbor model.</p

Variable importance plots for predictor variables (described in main text) from random forest classification of accelerometry data collected from a trained golden eagle.

<p>We classified data to (a) three behavioral classes: flapping, sitting and soaring and (b) five behavior classes: flapping banking, flapping straight, sitting, soaring banking and soaring straight. Higher values of mean decrease in accuracy indicate that the variables are more important to the classification process.</p

Ten-fold cross validation error rates for classification of accelerometer data collected from a trained golden eagle.

<p>Models tested were random forest (RF) and K-nearest neighbor (KNN).</p

Accuracy of behavioral classification accuracy when sampling acceleration data from a trained golden eagle from 5 to 40Hz.

<p>Data were classified to three behavioral classes (flapping, sitting and soaring) and modeled with (a) a random forest classification model and (b) a K-nearest neighbor model.</p

Overall classification accuracy using a K-nearest neighbor model to classify acceleration data from a trained golden eagle.

<p>We used both (a) a simple ethogram (three behavioral classes: flapping, sitting and soaring) and (b) a complex ethogram (five behavior classes: flapping banking, flapping straight, sitting, soaring banking and soaring straight). We incrementally increased values of K by 5 until classification accuracy declined and then incrementally adjusted values of K by 1 to identify peak accuracy, indicated with a box.</p