Uber Data Breach

Year and Location of  Breach : 2016, USA

Impact : 57 million Uber users and 600,000 drivers

Introduction : Uber Technologies Inc. is a peer-to-peer ridesharing, food delivery, and transportation network company headquartered in San Francisco, California, with operations in 633 cities worldwide. Its platforms can be accessed via its websites and mobile apps. However, in 2016, two hackers were able to get the names, email addresses, and mobile phone numbers of the 57 million users of the Uber app. They also got the driver license numbers of 600,000 Uber drivers. Uber as an organisation, instead of notifying affected drivers and users within time, paid hackers to delete data quietly. These irresponsible act attracted lawsuit from Pennsylvania Attorney General Josh Shapiro, for violating data breach notification law.

Reason : Uber had its database stored on Amazon Web Services(AWS). However, the username and password to the Uber’s AWS account was stored on Uber’s GitHub account. GitHub is a popular, online code-repository service based on the open-source version control system. The hackers were able to access the Uber’s GitHub account. Uber stated that their username and data was encrypted. But, GitHub stored the database in PostGres database. PostGres has a well-known potential weakness relating to "key disclosure issues" when encrypting database fields. Intruder found the credential [for AWS] contained within code on a private repository for Uber engineers on GitHub.

Solution : Uber removed its username and password from the GitHub. GitHub changed its programming interfaces that can be used to provide direct access to databases. GitHub made multi factor authentication recommended.

References:

[1]https://www.bankinfosecurity.com/uber-subpoenas-github-over-hack-attack-a-7963

[2]https://www.csoonline.com/article/2130877/data-breach/the-biggest-data-breaches-of-the-21st-century.html

Basic Operations on DSP Processor

We have seen the mathematical analysis and various digital systems with digital signals. However, they are of no use if they can't be used in the real world. Since, using a PC for real-time applications is cumbersome. DSP provides a perfect solution to this problem. It is capable of handling large mathematical operations easily while using real-time signals for the manipulations of the data. Thus, DSP can be interfaced with various real-time systems to form an integrated smart system. In this experiment, we acquainted with basic operations of the DSP processor C2000 and carried out siple mathematical operations such as addition, subtraction, bit manipulation,etc.

IEEE Paper Report: Automatic Speech Recognition using Correlation Analysis

This IEEE Paper is on matching the patterns of two audio files to recognize speech. This paper uses FFT and correlation to match the two speech files. As we know, correlation is used to determine the similarity between the two signals. Thus, by correlating the stored signal with the unknown signal, and finding the correlation coefficient the similarity between the two signals can be found out. If the correlation coefficient is close to unity, their exists a strong relation between the two signals and hence, they can be said to be forming same speech pattern.

Patent Review: Correlating Call Data and Speech Recognition Information in a Telephony

Very often speech recognition telephony applications such as Voice Response Systems (VRS) ask a caller to input alphanumeric information for their process. However, it is very inconvenient if the caller has limited display and input capabilities. Also, if one of the characters go wrong, complete call has to be repeated. Hence, there is a need of replacing this alphanumeric input to voice recognition systems. However, background noise and distortion is a hurdle in this area of development.

Patent No.: US 6,931,105 B2

FIR Filter Design using Windowing Method

Previous two blogs demonstrated the design of Digital IIR Filters. However, IIR filters are not stable due their recursive nature. Also, they have non-linear phase response hence, the output of the filter appears to be distorted version of the input.
To eliminate these issues FIR filters are used. They have Linear Phase response and hence, output of the filter is the phase shifted input signal. Also, since they don't have any feedback, the filters are very stable. However, this requires large memory as compared to IIR filters.
One of the method to design FIR filters is using Windowing Method. Other method is using FSM which will be shown in the next post. In this method, a particular window function is chosen on the basis of the attenuation required in the stop band. Using this window function, the coefficients of the filter is found and thus  filter is designed.

Butterworth Filter Design

In the previous blogs, we saw the implementation of various mathematical digital signal processing operations that can be performed on the input signal.
However, the main essence of a processor is to implement a digital filter which can emulate similar characteristics as that  of an analog filter.
Here, to simulate the behavior of digital filters, Scilab Software is used. Scilab provides an easy interface for coding and can also generate frequency response curves using in-built functions.
In this blog, a digital Butterworth Filter is implemented. A butterworth filter is a type of a signal processing filter designed to have a maximally flat magnitude response. Initially, the parameters of the required filter such  as Passband Attenuation, Passband Frequency, Stopband Attenuation and Stopband Frequency are inputted by the user. Based on these parameters, a digital butterworth filter (LPF or HPF) was designed using Bi-Linear Transformation(BLT) Method. Once, the digital filter was designed, order and transfer function was displayed and magnitude response of the filter was shown.

FIR Filter Design using FSM

FSM, i.e., Frequency Sampling Method is another method of designing an FIR Filter. FSM for FIR filter design is perhaps the simplest and most direct technique when a desired frequency response has been specified. It involves uniform sampling of the desired frequency response, and performing an inverse DFT to obtain the corresponding FIR response. The results are not optimal, however, because the response generally deviates from what is desired between the samples. When the desired frequency response is undersampled, the resulting impulse response will be time aliased to some extent.