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Prashant Krishnamurthy
University of Pittsburgh
168Publications
26H-index
3,418Citations
Publications 168
Newest
2018 in International Conference on Computer Communications
Yue Cao2
Estimated H-index: 2
(University of California),
Ahmed Osama Fathy Atya3
Estimated H-index: 3
(University of California)
+ 5 AuthorsLisa M. Marvel8
Estimated H-index: 8
(United States Army Research Laboratory)
Eavesdroppers can exploit exposed packet headers towards attacks that profile clients and their data flows. In this paper, we propose FOG, a framework for effective header blinding using MIMO, to thwart eavesdroppers. FOG effectively tracks header bits as they traverse physical (PHY) layer sub-systems that perform functions like scrambling and interleaving. It combines multiple blinding signals for more effective and less predictable obfuscation, as compared to using a fixed blinding signal. We ...
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2018
Mai Abdelhakim (University of Pittsburgh), Xin Liu (University of Pittsburgh), Prashant Krishnamurthy26
Estimated H-index: 26
(University of Pittsburgh)
This paper presents a diversity model for reliable transmission in multihop networks under routing attacks. Traditionally, diversity is envisioned to be best achieved by utilizing multiple independent and disjoint paths. In this paper, we show that increasing the dependency/connectivity among transmission paths enables accurate detection and identification of malicious nodes. We define a diversity metric that reflects the degree of connectivity among routing paths. Then, we analyze the detection...
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2018
Chao Li2
Estimated H-index: 2
(University of Pittsburgh),
Balaji Palanisamy10
Estimated H-index: 10
(University of Pittsburgh),
Prashant Krishnamurthy26
Estimated H-index: 26
(University of Pittsburgh)
The amount of digital data generated in the Big Data age is increasingly rapidly. Privacy-preserving data publishing techniques based on differential privacy through data perturbation provide a safe release of datasets such that sensitive information present in the dataset cannot be inferred from the published data. Existing privacy-preserving data publishing solutions have focused on publishing a single snapshot of the data with the assumption that all users of the data share the same level of ...
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2018 in The Internet of Things
Xin Liu (University of Pittsburgh), Mai Abdelhakim7
Estimated H-index: 7
(Michigan State University)
+ 1 AuthorsDavid Tipper26
Estimated H-index: 26
(University of Pittsburgh)
Packet manipulation attack is one of the challenging threats in cyber-physical systems (CPSs) and Internet of Things (IoT), where information packets are corrupted during transmission by compromised devices. These attacks consume network resources, result in delays in decision making, and could potentially lead to triggering wrong actions that disrupt an overall system's operation. Such malicious attacks as well as unintentional faults are difficult to locate/identify in a large-scale mesh-like ...
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2018 in International Conference on Communications
Xin Liu (University of Pittsburgh), Mai Abdelhakim7
Estimated H-index: 7
(Michigan State University)
+ 1 AuthorsDavid Tipper26
Estimated H-index: 26
(University of Pittsburgh)
The increased connectivity introduced in Internet of Things (IoT) applications makes such systems vulnerable to serious security threats. In this paper, we consider one of the most challenging threats in IoT networks, where devices manipulate (maliciously or unintentionally) the data transmitted in infor-mation packets as they are being forwarded from the source to the destination. We propose unsupervised learning that exploits network diversity to detect and identify suspicious networked elemen...
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2017 in International Conference on Big Data
Balaji Palanisamy10
Estimated H-index: 10
(University of Pittsburgh),
Chao Li2
Estimated H-index: 2
(University of Pittsburgh),
Prashant Krishnamurthy26
Estimated H-index: 26
(University of Pittsburgh)
In the age of Big Data, we are witnessing a huge proliferation of digital data capturing our lives and our surroundings. Data privacy is a critical barrier to data analytics and privacy-preserving data disclosure becomes a key aspect to leveraging large-scale data analytics due to serious privacy risks. Traditional privacy-preserving data publishing solutions have focused on protecting individual's private information while considering all aggregate information about individuals as safe for disc...
1 Citations Source Cite
2017
Martin B. H. Weiss15
Estimated H-index: 15
,
Prashant Krishnamurthy26
Estimated H-index: 26
,
Marcela Gomez3
Estimated H-index: 3
Spectrum policy in the US (and throughout most of the world) consists generally of a set of nationally determined policies that apply uniformly to all localities. However, it is also true that there is considerable variation in the features (e.g., traffic demand or population density), requirements and constraints of spectrum use on a local basis. Global spectrum policies designed to resolve a situation in New York City could well be overly restrictive for communities in rural areas (such as cen...
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2017
Vladimir Zadorozhny16
Estimated H-index: 16
(University of Pittsburgh),
Prashant Krishnamurthy26
Estimated H-index: 26
(University of Pittsburgh)
+ 2 AuthorsJiawei Xu
As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things.
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2017 in International Conference on Distributed Computing Systems
Balaji Palanisamy10
Estimated H-index: 10
(University of Pittsburgh),
Chao Li2
Estimated H-index: 2
(University of Pittsburgh),
Prashant Krishnamurthy26
Estimated H-index: 26
(University of Pittsburgh)
Traditional privacy-preserving data disclosure solutions have focused on protecting the privacy of individual's information with the assumption that all aggregate (statistical) information about individuals is safe for disclosure. Such schemes fail to support group privacy where aggregate information about a group of individuals may also be sensitive and users of the published data may have different levels of access privileges entitled to them. We propose the notion of Eg-Group Differential Pri...
2 Citations Source Cite
2017
Maryam Karimi (University of Pittsburgh), Prashant Krishnamurthy26
Estimated H-index: 26
(University of Pittsburgh)
+ 1 AuthorsDavid Tipper26
Estimated H-index: 26
(University of Pittsburgh)
WiFi networks often seek to reduce interference through network planning, macroscopic self-organization (e.g. channel switching) or network management. In this paper, we explore the use of historical data to automatically predict traffic bottlenecks and make rapid decisions in a wireless (WiFi-like) network on a smaller scale. This is now possible with software defined networks (SDN), whose controllers can have a global view of traffic flows in a network. Models such as classification trees can ...
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