Zobrazeno 1 - 10
of 260
pro vyhledávání: '"VENKATASUBRAMANIAN, NALINI"'
This paper presents the design and implementation of FLIPS, a middleware system to manage data and participant heterogeneity in federated learning (FL) training workloads. In particular, we examine the benefits of label distribution clustering on par
Externí odkaz:
http://arxiv.org/abs/2308.03901
Existing federated learning models that follow the standard risk minimization paradigm of machine learning often fail to generalize in the presence of spurious correlations in the training data. In many real-world distributed settings, spurious corre
Externí odkaz:
http://arxiv.org/abs/2211.01914
Autor:
Panwar, Nisha, Sharma, Shantanu, Wang, Guoxi, Mehrotra, Sharad, Venkatasubramanian, Nalini, Diallo, Mamadou H., Sani, Ardalan Amiri
Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced -- IoT systems may violate the rules deliberately or IoT devices
Externí odkaz:
http://arxiv.org/abs/2108.02293
This paper proposes a system, entitled Concealer that allows sharing time-varying spatial data (e.g., as produced by sensors) in encrypted form to an untrusted third-party service provider to provide location-based applications (involving aggregation
Externí odkaz:
http://arxiv.org/abs/2102.05238
Autor:
Gupta, Peeyush, Mehrotra, Sharad, Panwar, Nisha, Sharma, Shantanu, Venkatasubramanian, Nalini, Wang, Guoxi
Contact tracing has emerged as one of the main mitigation strategies to prevent the spread of pandemics such as COVID-19. Recently, several efforts have been initiated to track individuals, their movements, and interactions using technologies, e.g.,
Externí odkaz:
http://arxiv.org/abs/2005.02510
Autor:
Lin, Yiming, Jiang, Daokun, Yus, Roberto, Bouloukakis, Georgios, Chio, Andrew, Mehrotra, Sharad, Venkatasubramanian, Nalini
This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi connectivity data consists of sporadic connections between devices and ne
Externí odkaz:
http://arxiv.org/abs/2004.09676
This paper focuses on the new privacy challenges that arise in smart homes. Specifically, the paper focuses on inferring the user's activities -- which may, in turn, lead to the user's privacy -- via inferences through device activities and network t
Externí odkaz:
http://arxiv.org/abs/2004.03841
Autor:
Panwar, Nisha, Sharma, Shantanu, Gupta, Peeyush, Ghosh, Dhrubajyoti, Mehrotra, Sharad, Venkatasubramanian, Nalini
The growing deployment of Internet of Things (IoT) systems aims to ease the daily life of end-users by providing several value-added services. However, IoT systems may capture and store sensitive, personal data about individuals in the cloud, thereby
Externí odkaz:
http://arxiv.org/abs/2003.04969
Autor:
Panwar, Nisha, Sharma, Shantanu, Wang, Guoxi, Mehrotra, Sharad, Venkatasubramanian, Nalini, Diallo, Mamadou H., Sani, Ardalan Amiri
Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced --- IoT systems may violate the rules deliberately or IoT device
Externí odkaz:
http://arxiv.org/abs/1908.10033
Autor:
Panwar, Nisha, Sharma, Shantanu, Mehrotra, Sharad, Krzywiecki, Łukasz, Venkatasubramanian, Nalini
Smart homes are a special use-case of the Internet-of-Things (IoT) paradigm. Security and privacy are two prime concern in smart home networks. A threat-prone smart home can reveal lifestyle and behavior of the occupants, which may be a significant c
Externí odkaz:
http://arxiv.org/abs/1904.05476