http://repository.iiitd.edu.in/xmlui/handle/123456789/123 2026-04-11T17:00:59Z 2026-04-11T17:00:59Z Verma, Robin Gupta, Gaurav http://repository.iiitd.edu.in/xmlui/handle/123456789/210 2017-07-24T17:15:35Z 2014-12-25T07:23:25Z

Perception of data privacy in digital forensic investigation Verma, Robin; Gupta, Gaurav The use of digital technology in conventional as well as new age crimes is increasing throughout the world, and researchers are working to develop digital forensics techniques to investigate such crimes. Digital forensic investigation process requires the digital forensic investigator to manually examine the forensic image of the seized storage media. The investigator gets full access of all the data contained in the forensic image including the accused or the victim’s private or sensitive data that may be entirely unrelated to the given case. The unrestricted access on the seized media or its forensic image becomes a significant threat to the accused or the victim’s data privacy as the investigator can view or copy the data at will. There is no legal or technical infrastructure in place to stop such abuse. The paper presents a study containing three different surveys for the three stakeholders in the digital forensic investigation and aim to capture their perception of the accused or the victim’s data privacy. The surveys were circulated in India among the digital forensic investigators, cyber lawyers and the general public respectively. These surveys included questions related to the privacy of digital data present on the storage media during the course of digital forensic investigation and subsequent trial in court of law. The surveys collected 15, 10 and 1889 responses from participants of respective target audience classes. The responses show lack of professional ethics among investigators, lack of legal support for lawyers to protect data privacy during digital forensic investigation and confusion among the general public regarding their data privacy rights. The findings would help in justifying the need for privacy preserving digital forensic investigation framework that protect privacy during the digital forensic investigation process without compromising on efficiency and performance.

2014-12-25T07:23:25Z Gautam, Alvika Naik, Vinayak Gupta, Archie Sharma, SK http://repository.iiitd.edu.in/xmlui/handle/123456789/197 2017-07-24T17:15:09Z 2014-09-17T08:14:38Z

An smartphone-based algorithm to measure and model quantity of sleep Gautam, Alvika; Naik, Vinayak; Gupta, Archie; Sharma, SK Sleep quantity affects an individual’s personal health. The gold standard of measuring sleep and diagnosing sleep disorders is Polysomnography (PSG). Although PSG is accurate, it is expensive and it lacks portability. A number of wearable devices with embedded sensors have emerged in the recent past as an alternative to PSG for regular sleep monitoring directly by the user. These devices are intrusive and cause discomfort besides being expensive. In this work, we present an algorithm to detect sleep using a smartphone with the help of its inbuilt accelerometer sensor. We present three different approaches to classify raw acceleration data into two states - Sleep and Wake. In the first approach, we take an equation from Kushida’s algorithm to process accelerometer data. Henceforth, we call it Kushida’s equation. While the second is based on statistical functions, the third is based on Hidden Markov Model (HMM) training. Although all the three approaches are suitable for a phone’s resources, each approach demands different amount of resources. While Kushida’s equation-based approach demands the least, the HMM training-based approach demands the maximum. We collected data from mobile phone’s accelerometer for four subjects for twelve days each. We compare accuracy of sleep detection using each of the three approaches with that of Zeo sensor, which is based on Electroencephalogram (EEG) sensor to detect sleep. EEG is an important modality in PSG. We find that HMM training-based approach is as much as 84% accurate. It is 15% more accurate as compared to Kushida’s equation-based approach and 10% more accurate as compared to statistical method-based approach. In order to concisely represent the sleep quality of people, we model their sleep data using HMM. We present an analysis to find out a tradeoff between the amount of training data and the accuracy provided in the modeling of sleep. We find that six days of sleep data is sufficient for accurate modeling. We compare accuracy of our HMM trainingbased algorithm with a representative third party app SleepTime available from Google Play Store for Android. We find that the detection done using HMM approach is closer to that done by Zeo by 13% as compared to the third party Android application SleepTime. We show that our HMM training-based approach is efficient as it takes less than ten seconds to get executed on Moto G Android phone.

2014-09-17T08:14:38Z Hemani, Rakhi Banerjee, Subhasis Guha, Apala http://repository.iiitd.edu.in/xmlui/handle/123456789/196 2017-07-24T17:15:45Z 2014-09-15T10:56:05Z

ACCORD : an analytical cache contention model using reuse distances for modern multiprocessors Hemani, Rakhi; Banerjee, Subhasis; Guha, Apala Simultaneous execution of multiple threads on multicores is necessary for good resource utilization. However, such utilization calls for accurate models to predict the impact on performance due to contention of shared resources, primarily the last level cache and memory. The major challenges in developing such a model for commercial multicore machines are the unavailability of cache implementation details and the scalability of the performance prediction model for multiple threads. In this paper we propose a cache contention model addressing both these challenges. We leverage observed cache behaviour and reuse distance profile of applications for this purpose. We implement our model on a Xeon Sandy Bridge multicore and observe an RMS error of less than 0.06, for single threaded and multi-threaded workloads. Further we compare the effectiveness of using ACCORD model against the popular LRU Model and find that ACCORD is upto 2.7 times more accurate.

2014-09-15T10:56:05Z Mishra, Shreemoy http://repository.iiitd.edu.in/xmlui/handle/123456789/160 2017-07-24T17:15:08Z 2014-07-16T11:30:00Z

Consumer privacy and targeted pricing with stochastic valuations Mishra, Shreemoy I study the market for consumer information. Firms cannot commit to privacy, so buyers anticipate disclosure of purchase history for targeted pricing. In the literature, strategic rejections of informative o ers make purchase data worthless, which is paradoxical given the large investments in data analytics. I show that buyers' uncertainty regarding future preferences allows full separation of types, leading to valuable purchase data. Buyers of any given type are assumed to share similar expectations about the evolution of future valuations. Preference uncertainty generates consumer-to-consumer externalities, which is why strategic consumers are sometimes better o when they remain unaware of targeted pricing. When preferences are transitory, rms have an incentive to raise consumer awareness about targeted pricing.

2014-07-16T11:30:00Z