An Efficiently Searchable Encrypted Data Structure for Range Queries

At CCS 2015 Naveed et al. presented first attacks on efficiently searchable encryption, such as deterministic and order-preserving encryption. These plaintext guessing attacks have been further improved in subsequent work, e.g. by Grubbs et al. in 2016. Such cryptanalysis is crucially important to sharpen our understanding of the implications of security models. In this paper we present an efficiently searchable, encrypted data structure that is provably secure against these and even more powerful chosen plaintext attacks. Our data structure supports logarithmic-time search with linear space complexity. The indices of our data structure can be used to search by standard comparisons and hence allow easy retrofitting to existing database management systems. We implemented our scheme and show that its search time overhead is only 10 milliseconds compared to non-secure search

Deep Learning Based Caching for Self-Driving Car in Multi-access Edge Computing

Once self-driving car becomes a reality and passengers are no longer worry about it, they will need to find new ways of entertainment. However, retrieving entertainment contents at the Data Center (DC) can hinder content delivery service due to high delay of car-to-DC communication. To address these challenges, we propose a deep learning based caching for self-driving car, by using Deep Learning approaches deployed on the Multi-access Edge Computing (MEC) structure. First, at DC, Multi-Layer Perceptron (MLP) is used to predict the probabilities of contents to be requested in specific areas. To reduce the car-DC delay, MLP outputs are logged into MEC servers attached to roadside units. Second, in order to cache entertainment contents stylized for car passengers' features such as age and gender, Convolutional Neural Network (CNN) is used to predict age and gender of passengers. Third, each car requests MLP output from MEC server and compares its CNN and MLP outputs by using k-means and binary classification. Through this, the self-driving car can identify the contents need to be downloaded from the MEC server and cached. Finally, we formulate deep learning based caching in the self-driving car that enhances entertainment services as an optimization problem whose goal is to minimize content downloading delay. To solve the formulated problem, a Block Successive Majorization-Minimization (BS-MM) technique is applied. The simulation results show that the accuracy of our prediction for the contents need to be cached in the areas of the self-driving car is achieved at 98.04% and our approach can minimize delay

Zwei Anwendungen des Paillier-Kryptosystems: Blinde Signatur und Three-Pass-Protocol

Englisch: In this paper we study the paillier cryptosystem and derive form it to new schemes. First we transform the signature of paillier in a Blind signature. Secondly we propose a three-pass protocol wich use the homomorphic property instead of the commutativity as the Shamir protocol does. German: Basierend auf dem Kryptosystem von Paillier und dem damit eingef\"uhrten Problem der zusammengesetzten Residuenklasse werden in diesem Artikel zwei kryptographische Verfahren vorgeschlagen. Zun\"achst wird die Signatur von Paillier in ein blindes Signaturverfahren umgewandelt. Des Weiteren wird mit der homomorphen Eigenschaft des Kryptosystems von Paillier ein sogenanntes Three-Pass-Protocol - auch No-Key-Protocol genannt - entwickelt.Comment: 10 page