Year-2014http://repository.iiitd.edu.in/xmlui/handle/123456789/1232024-03-29T05:59:04Z2024-03-29T05:59:04ZPerception of data privacy in digital forensic investigationVerma, RobinGupta, Gauravhttp://repository.iiitd.edu.in/xmlui/handle/123456789/2102017-07-24T17:15:35Z2014-12-25T07:23:25ZPerception 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.
2014-12-25T07:23:25ZAn smartphone-based algorithm to measure and model quantity of sleepGautam, AlvikaNaik, VinayakGupta, ArchieSharma, SKhttp://repository.iiitd.edu.in/xmlui/handle/123456789/1972017-07-24T17:15:09Z2014-09-17T08:14:38ZAn 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.
2014-09-17T08:14:38ZACCORD : an analytical cache contention model using reuse distances for modern multiprocessorsHemani, RakhiBanerjee, SubhasisGuha, Apalahttp://repository.iiitd.edu.in/xmlui/handle/123456789/1962017-07-24T17:15:45Z2014-09-15T10:56:05ZACCORD : 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.
2014-09-15T10:56:05ZConsumer privacy and targeted pricing with stochastic valuationsMishra, Shreemoyhttp://repository.iiitd.edu.in/xmlui/handle/123456789/1602017-07-24T17:15:08Z2014-07-16T11:30:00ZConsumer 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