Balaji Palanisamy

Bio: Balaji Palanisamy is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Cloud computing & Information privacy. The author has an hindex of 17, co-authored 86 publications receiving 1485 citations. Previous affiliations of Balaji Palanisamy include University UCINF & Georgia Institute of Technology.

MobiMix: Protecting location privacy with mix-zones over road networks

Abstract: This paper presents MobiMix, a road network based mix-zone framework to protect location privacy of mobile users traveling on road networks. In contrast to spatial cloaking based location privacy protection, the approach in MobiMix is to break the continuity of location exposure by using mix-zones, where no applications can trace user movement. This paper makes two original contributions. First, we provide the formal analysis on the vulnerabilities of directly applying theoretical rectangle mix-zones to road networks in terms of anonymization effectiveness and attack resilience. We argue that effective mix-zones should be constructed and placed by carefully taking into consideration of multiple factors, such as the geometry of the zones, the statistical behavior of the user population, the spatial constraints on movement patterns of the users, and the temporal and spatial resolution of the location exposure. Second, we develop a suite of road network mix-zone construction methods that provide higher level of attack resilience and yield a specified lower-bound on the level of anonymity. We evaluate the MobiMix approach through extensive experiments conducted on traces produced by GTMobiSim on different scales of geographic maps. Our experiments show that MobiMix offers high level of anonymity and high level of resilience to attacks compared to existing mix-zone approaches.

Purlieus: locality-aware resource allocation for MapReduce in a cloud

Abstract: We present Purlieus, a MapReduce resource allocation system aimed at enhancing the performance of MapReduce jobs in the cloud. Purlieus provisions virtual MapReduce clusters in a locality-aware manner enabling MapReduce virtual machines (VMs) access to input data and importantly, intermediate data from local or close-by physical machines. We demonstrate how this locality-awareness during both map and reduce phases of the job not only improves runtime performance of individual jobs but also has an additional advantage of reducing network traffic generated in the cloud data center. This is accomplished using a novel coupling of, otherwise independent, data and VM placement steps. We conduct a detailed evaluation of Purlieus and demonstrate significant savings in network traffic and almost 50% reduction in job execution times for a variety of workloads.

Zenith: Utility-Aware Resource Allocation for Edge Computing

Abstract: In the Internet of Things(IoT) era, the demands for low-latency computing for time-sensitive applications (e.g., location-based augmented reality games, real-time smart grid management, real-time navigation using wearables) has been growing rapidly. Edge Computing provides an additional layer of infrastructure to fill latency gaps between the IoT devices and the back-end computing infrastructure. In the edge computing model, small-scale micro-datacenters that represent ad-hoc and distributed collection of computing infrastructure pose new challenges in terms of management and effective resource sharing to achieve a globally efficient resource allocation. In this paper, we propose Zenith, a novel model for allocating computing resources in an edge computing platform that allows service providers to establish resource sharing contracts with edge infrastructure providers apriori. Based on the established contracts, service providers employ a latency-aware scheduling and resource provisioning algorithm that enables tasks to complete and meet their latency requirements. The proposed techniques are evaluated through extensive experiments that demonstrate the effectiveness, scalability and performance efficiency of the proposed model.

Cost-Effective Resource Provisioning for MapReduce in a Cloud

Abstract: This paper presents a new MapReduce cloud service model, Cura, for provisioning cost-effective MapReduce services in a cloud. In contrast to existing MapReduce cloud services such as a generic compute cloud or a dedicated MapReduce cloud, Cura has a number of unique benefits. First, Cura is designed to provide a cost-effective solution to efficiently handle MapReduce production workloads that have a significant amount of interactive jobs. Second, unlike existing services that require customers to decide the resources to be used for the jobs, Cura leverages MapReduce profiling to automatically create the best cluster configuration for the jobs. While the existing models allow only a per-job resource optimization for the jobs, Cura implements a globally efficient resource allocation scheme that significantly reduces the resource usage cost in the cloud. Third, Cura leverages unique optimization opportunities when dealing with workloads that can withstand some slack. By effectively multiplexing the available cloud resources among the jobs based on the job requirements, Cura achieves significantly lower resource usage costs for the jobs. Cura’s core resource management schemes include cost-aware resource provisioning, VM-aware scheduling and online virtual machine reconfiguration. Our experimental results using Facebook-like workload traces show that our techniques lead to more than 80 percent reduction in the cloud compute infrastructure cost with upto 65 percent reduction in job response times.

Attack-Resilient Mix-zones over Road Networks: Architecture and Algorithms

Abstract: Continuous exposure of location information, even with spatially cloaked resolution, may lead to breaches of location privacy due to statistics-based inference attacks. An alternative and complementary approach to spatial cloaking based location anonymization is to break the continuity of location exposure by introducing techniques, such as mix-zones, where no application can trace user movements. Several factors impact on the effectiveness of mix-zone approach, such as user population, mix-zone geometry, location sensing rate and spatial resolution, as well as spatial and temporal constraints on user movement patterns. However, most of the existing mix-zone proposals fail to provide effective mix-zone construction and placement algorithms that are resilient to timing and transition attacks. This paper presents MobiMix, a road network based mix-zone framework to protect location privacy of mobile users traveling on road networks. It makes three original contributions. First, we provide the formal analysis on the vulnerabilities of directly applying theoretical rectangle mix-zones to road networks in terms of anonymization effectiveness and resilience to timing and transition attacks. Second, we develop a suite of road network mix-zone construction methods that effectively consider the above mentioned factors to provide higher level of resilience to timing and transition attacks, and yield a specified lower-bound on the level of anonymity. Third, we present a set of mix-zone placement algorithms that identify the best set of road intersections for mix-zone placement considering the road network topology, user mobility patterns and road characteristics. We evaluate the MobiMix approach through extensive experiments conducted on traces produced by GTMobiSim on different scales of geographic maps. Our experiments show that MobiMix offers high level of anonymity and high level of resilience to timing and transition attacks, compared to existing mix-zone approaches.

Optical burst switching (OBS) - a new paradigm for an optical Internet

Abstract: To support bursty traffic on the Internet (and especially WWW) efficiently, optical burst switching (OBS) is proposed as a way to streamline both protocols and hardware in building the future gener...

General data protection regulation

Abstract: Presentacio sobre l'Oficina de Proteccio de Dades Personals de la UAB i la politica Open Science. Va formar part de la conferencia "Les politiques d'Open Data / Open Acces: Implicacions a la recerca" orientada a investigadors i gestors de projectes europeus que va tenir lloc el 20 de setembre de 2018 a la Universitat Autonoma de Barcelona

Privacy preserving association rule mining in vertically partitioned data

Abstract: A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed