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Register a new userPretzel: Email encryption and provider-supplied functions are compatible
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Added by
Trinabh Gupta
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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Emails today are often encrypted, but only between mail servers---the vast majority of emails are exposed in plaintext to the mail servers that handle them. While better than no encryption, this arrangement leaves open the possibility of attacks, privacy violations, and other disclosures. Publicly, email providers have stated that default end-to-end encryption would conflict with essential functions (spam filtering, etc.), because the latter requires analyzing email text. The goal of this paper is to demonstrate that there is no conflict. We do so by designing, implementing, and evaluating Pretzel. Starting from a cryptographic protocol that enables two parties to jointly perform a classification task without revealing their inputs to each other, Pretzel refines and adapts this protocol to the email context. Our experimental evaluation of a prototype demonstrates that email can be encrypted end-to-end emph{and} providers can compute over it, at tolerable cost: clients must devote some storage and processing, and provider overhead is roughly 5 times versus the status quo.
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Information security is of great importance for modern society with all things connected. Physical unclonable function (PUF) as a promising hardware primitive has been intensively studied for information security. However, the widely investigated silicon PUF with low entropy is vulnerable to various attacks. Herein, we introduce a concept of bionic optical PUFs inspired from unique biological architectures, and fabricate four types of bionic PUFs by molding the surface micro-nano structures of natural plant tissues with a simple, low-cost, green and environmentally friendly manufacturing process. The laser speckle responses of all bionic PUFs are statistically demonstrated to be random, unique, unpredictable and robust enough for cryptographic applications, indicating the broad applicability of bionic PUFs. On this ground, the feasibility of implementing bionic PUFs as cryptographic primitives in entity authentication and encrypted communication is experimentally validated, which shows its promising potential in the application of future information security.
This work proposes a different procedure to encrypt images of 256 grey levels and colour, using the symmetric system Advanced Encryption Standard with a variable permutation in the first round, after the x-or operation. Variable permutation means using a different one for each input block of 128 bits. In this vein, an algorithm is constructed that defines a Bijective function between sets Nm = {n in N, 0 <= n < fac(m)} with n >= 2 and Pm = {pi, pi is a permutation of 0, 1, ..., m-1}. This algorithm calculates permutations on 128 positions with 127 known constants. The transcendental numbers are used to select the 127 constants in a pseudo-random way. The proposed encryption quality is evaluated by the following criteria: Correlation; horizontal, vertical and diagonal, Entropy and Discrete Fourier Transform. The latter uses the NIST standard 800-22. Also, a sensitivity analysis was performed in encrypted figures. Furthermore, an additional test is proposed which considers the distribution of 256 shades of the three colours; red, green and blue for colour images. On the other hand, it is important to mention that the images are encrypted without loss of information because many banking companies and some safety area countries do not allow the figures to go through a compression process with information loss. i.e., it is forbidden to use formats such as JPEG.
The popularity, cost-effectiveness and ease of information exchange that electronic mails offer to electronic device users has been plagued with the rising number of unsolicited or spam emails. Driven by the need to protect email users from this growing menace, research in spam email filtering/detection systems has being increasingly active in the last decade. However, the adaptive nature of spam emails has often rendered most of these systems ineffective. While several spam detection models have been reported in literature, the reported performance on an out of sample test data shows the room for more improvement. Presented in this research is an improved spam detection model based on Extreme Gradient Boosting (XGBoost) which to the best of our knowledge has received little attention spam email detection problems. Experimental results show that the proposed model outperforms earlier approaches across a wide range of evaluation metrics. A thorough analysis of the model results in comparison to the results of earlier works is also presented.
As the application of deep learning continues to grow, so does the amount of data used to make predictions. While traditionally, big-data deep learning was constrained by computing performance and off-chip memory bandwidth, a new constraint has emerged: privacy. One solution is homomorphic encryption (HE). Applying HE to the client-cloud model allows cloud services to perform inference directly on the clients encrypted data. While HE can meet privacy constraints, it introduces enormous computational challenges and remains impractically slow in current systems. This paper introduces Cheetah, a set of algorithmic and hardware optimizations for HE DNN inference to achieve plaintext DNN inference speeds. Cheetah proposes HE-parameter tuning optimization and operator scheduling optimizations, which together deliver 79x speedup over the state-of-the-art. However, this still falls short of plaintext inference speeds by almost four orders of magnitude. To bridge the remaining performance gap, Cheetah further proposes an accelerator architecture that, when combined with the algorithmic optimizations, approaches plaintext DNN inference speeds. We evaluate several common neural network models (e.g., ResNet50, VGG16, and AlexNet) and show that plaintext-level HE inference for each is feasible with a custom accelerator consuming 30W and 545mm^2.
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