In many countries, energy consumption data is collected through smart meters in 15-min intervals. Prior work has shown that 1 year’s worth of this data is sufficient to extract sensitive information about households. In this short paper, we break down energy consumption data from a novel dataset into 1-week snippets. Using off-the-shelf algorithms, we assess whether it is possible to clearly identify (i.e., fingerprint) an individual household only by its energy consumption from a 1-week period. More generally, we ask whether an attacker can distinguish one household from a group of others by its energy consumption from only one week’s worth of data. We find that a small number of households exist for which the weekly consumption is so unique that it can be distinguished almost always amidst weekly data from dozens of other households. Furthermore, a large number of households can be distinguished with surprisingly high accuracy and an order of magnitude better than guessing. We discuss the potential impact of these findings on the privacy of smart meter datasets with respect to de-anonymization and re-identifiability.

Reading text, identifying key ideas, summarizing, making connections and other tasks that require comprehension and context are easy tasks for humans, but training a computer to perform these tasks is a challenge. Recent advances in deep learning make it possible to interpret the text effectively and achieve high performance results across natural language tasks. Interacting with relational databases trough natural language enables users of any background to query and analyze a huge amount of data in a user-friendly way. The purpose of Natural Language Interface is to allow users to compose questions in Natural Language and receive the response also in Natural Language. The idea of using natural language instead of SQL has promoted the development of new type of processing called Natural Language Interface to Database (NLIDB). This paper is an introduction to Natural Language Processing and Natural Language Interface to Database, significant challenges in this research field and how to construct a company specific dataset. It also gives a brief overview of the major techniques used to develop Natural Language Interface to Databases.

Reading text, identifying key ideas, summarizing, making connections and other tasks that require comprehension and context are easy tasks for humans but training a computer to perform these tasks is a challenge. Recent advances in deep learning make it possible to interpret text effectively and achieve high performance results across natural language tasks. Interacting with relational databases trough natural language enables users of any background to query and analyze a huge amount of data in a user-friendly way. This paper summaries major challenges and different approaches in the context of Natural Language Interfaces to Databases (NLIDB). A state-ofthe- art language translation model developed by Google named Transformer is used to translate natural language queries into structured queries to simplify the interaction between users and relational database systems.