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NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
FACULTY BRIEFINGS
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Wireless Communications
and Networking
M. CENK GURSOY
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
Wireless World
• There were 4.3 billion global mobile users in
2014. 5.2 billion users are predicted by 2019.
• Global mobile data grew 69% in 2014 and
reached 2.5 exabytes (1018) per month.
• Almost half a billion (497 million) mobile devices
and connections were added in 2014.
• It is predicted that there will be 11 billion mobileconnected devices by 2019.
• Mobile video traffic grew to 55% by the end
of 2014 and will represent 72% of the total
mobile data traffic by 2019.
2
Wireless Challenges
•Wireless medium is a timevarying, open, shared, and
broadcast medium.
•Resources, such as energy and
bandwidth, are limited and have to
be efficiently used.
•Exponential growth in mobile data
and multimedia content further
strains the limited resources.
•We are experiencing a spectrum
crunch, and carbon footprint of
wireless services is growing.
3
1. Green Wireless Communications
•Due to limited energy available for mobile
units, rising energy costs, and environmental
concerns, energy efficiency in wireless
systems is a critical concern.
•We investigate
–Energy-efficient wireless transmission
schemes,
–Energy-efficient resource allocation strategies,,
–Energy-efficient power control policies,
–Energy-efficient networking protocols,
–Wireless power transfer, RF energy harvesting,
and wireless-powered networks.
4
2. Cognitive Radio and mmWave Systems
•Prime portion of the radio spectrum
(30 MHz – 3 GHz) has already been
allocated to specific services and
applications.
•We study two solutions to the
spectrum scarcity problem:
–Cognitive radio systems to overcome
spectrum underutilization
•Channel sensing errors, interference
constraints, throughput, error rates.
–Moving to the less explored, less
utilized millimeter wave band (30 –
300 GHz)
5
3. Multimedia over Wireless Networks
•Multimedia traffic (e.g., VoIP, video
streaming, interactive video, online
gaming) requires certain quality of
service guarantees.
–We take into account the randomness
of the multimedia traffic and
investigate the throughput under
statistical delay/buffer constraints.
–We design multimedia transmissionreception systems and analyze the
quality of received images and videos.
–We determine efficient resource
allocation strategies to maximize the
performance and multimedia quality.
6
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Fighting Android Malware
Heng Yin
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
1
Android Malware: An Increasing Threat
McAfee Threat Report:
Totaled 3.73 million samples at the end of 2013, a 197% increase over 2012
2
Malware Variants and Zero-day Malware
McAfee Threat Report:
2.47 million new mobile malware samples were collected in 2013
3
My Research Samples
• DroidScope [USENIX Security 2012]
– For security analysts
– Understand malware’s innerworkings
• DroidSIFT [ACM CCS 2014]
– For app markets & antivirus scanners
– Detect zero-day malware and new variants
• DescribeMe [under submission]
– For end users
– Generate security-centric description
– Deter installation of malware
4
DroidSIFT
• Two key aspects
– Extract Weighted Contextual API Dependency Graphs as program
semantics
– Introduce Graph Similarity metrics to address malware variants
and zero-day malware
6
Evaluation: Classification Results
• Detection of Transformation Attacks
7
DescribeMe: Automatic Generation of SecurityCentric Description
When downloading an app, a user sees the following:
8
This is our description
The app retrieves your phone number
and writes to output depending on if the
user selects Button and prepares to write
data to network.
The app retrieves text from user input
and writes to output depending on if the
user selects button and prepares to write
data to network.
9
Impacts of New Descriptions
App Download Rates
Old Description
New Description
Malware
63.4%
24.7%
App /w Privacy Leak
80%
28.2%
Clean Apps
71.1%
59.3%
10
Questions?
http://lcs.syr.edu/faculty/yin
11
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Jae C. Oh
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
Distributed Multi-Agent Laboratory
• Big Data: Analysis, Computations, and Visualizations
– Recommender Systems and Reputation Management
– Visualizations
– Resource management in Cloud computing
• Massively Multi-agent and Multi-Robot Problems
– Autonomous Agents and Robots For Search and Rescue
– Distributed Task Allocation
– Game Theoretical Cyber-Physical Systems
2
Reasoning About Interactions and
Relationships
•
Reasoning/predicting about
what each rational agent
would do in a particular
situation (e.g., decision
support)
•
Anti-terrorism, rapid
reallocation of resources.
•
Finding robust
communication, resource
routing in a damaged
infrastructure.
•
Credit Card Fraud Detection
through cost/benefit
analysis
•
Spam filtering, identifying
important
document/information.
3
Big Data Fusion and Visualizations
•
•
•
•
•
Analysts working the information security space are confronted with a number
of challenges in their efforts to collect, process, and analyze data in order to
produce timely, accurate, and meaningful intelligence for the protection of
computing infrastructure and systems. The volume and diversity of both open
source and proprietary data available make it increasingly difficult for the
analyst to collect and process.
The intent of the project is to produce a prototype fusion and visualization
system. A prospective system would be capable of aggregating federated
structured and unstructured data. The system would perform multi-level fusion
wherein raw data with differing origin, format, content, and context receive the
appropriate level of processing for effective relationship and predicative
analytics. Fused data would then be used to synthesize intelligence which would
be represented in a number of manners including visualization and alerts
The anticipated prototype will provide two new capabilities to the Cyber Threat
and Security Operation Analysts. The first is derived from the multi-level fusion
wherein key pieces of data gleaned from highly structured network telemetry is
enriched with unstructured data and semantic meaning and used as
measurement for acceptable, unacceptable, and anonymous communication and
behavior of networked systems. The second is the representation of fused data
to provide the analyst more flexible and intuitive approaches to comprehend and
correlate activities while investigating incidents of interest.
The team will research solutions to challenges in the collection and processing of
data necessary to detect information security incidents and fraudulent online
banking transactions. The outcome will be a prototype system which JPMC’s
analysts may use to comprehend complex data, investigate, and improve
downstream alerting on issues of interest.
Most organizations have limited capability to identify and extract key attributes
and leverage them for analysis as well as guiding further collection. The analysis
and reporting processes are impacted by limited options for representing and
visualizing data resulting in slower or incomplete analysis and comprehension of
impact.
4
Massively Distributed Multi-Robots
• Goal: Design software and hardware that
work in the real physical world.
Example: Search and Rescue
5
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
G-Storm: A GPU-accelerated Platform for
Online Big Stream Data Processing
Jian Tang
Electrical Engineering and Computer Science
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
A General Platform for Online Big Stream Data
Processing (Storm/S4/TimeStream/MillWheel)
2
Challenges
GPU for Online Stream Data Processing, really?
• Different programming models
• Different data processing models
• Can GPU’s new features be utilized?
3
GPU-accelerated Big Stream Data Processing
4
G-Storm: Desirable Features
G-Storm: the first GPU-accelerated general-purpose parallel platform for online
big stream data processing.
•
•
•
•
A general (rather than application-specific) data processing platform as Storm,
which can deal with various applications and data types.
Exposing GPUs to Storm applications while preserving its easy-to-use
programming model by handling parallelization, data transfer and resource
allocation automatically via a runtime system without user involvement.
Achieving high-throughput and low-overhead data processing by buffering
multiple data tuples and offloading them in a batch to GPUs for highly-parallel
processing.
Accelerating data processing further by supporting Direct Data Transfer (DDT)
between two executors co-located on a common GPU.
5
G-Storm: Software Architecture
6
G-Storm: Experimental Results with the
Continuous Query App
7
G-Storm: Experimental Results with the
Continuous Query App
8
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Kevin Du
Cybersecurity
FACULTY BRIEFINGS
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
The Summary of My Work
Attack
Defense
Research
Education
Attacks on Smartphone
• Objectives:
– Systematically study the design of
Android system
– Find attacks
– Understand Fundamental causes
– Solution and Automatic detection
Code Injection Attack
• Discoveries:
– WebView (2011, led to a grant from
Google)
– Code Injection (2014)
– Multi-user Framework (2014)
– Clipboard (2014)
– Data Residue (2015)
– Dangerous Assumption (2015)
News Coverage
Re-design Android’s Security
• Objectives:
Protect Location
– Systematically study the
weakness of the security design in
Android
– Develop innovative solutions to
improve the design
– Get them adopted by Android
• Outcomes
– Isolate ads from apps
– SEIntent Firewall (undergrad)
– Resource isolation
Input Method
Security Education
• Objective
– Develop lab exercises
• Outcome
–
–
–
–
–
–
–
–
13 years：since 2002
$1.3 million: 3 NSF grants
30: number of labs developed
700 pages of lab materials
$0: cost of using these labs
250 schools
30 countries
70,000 hits per month
• This Summer
– 60 instructors come to SU for training
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Kishan Mehrotra
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
Why Big Data Analysis?
More data -> More
information
Competitive
advantage
Hidden patterns,
unknown correlations
Effective marketing,
customer satisfaction,
increased revenue
Better business
decisions
Dynamic nature of
data
2
Key Player problem: Large Social Networks
• Existing single objective key player
identification algorithms – Eigen-value
Centrality, Closeness Centrality, …
• Multi objective algorithm out performs
traditional single objective algorithms
– time complexity is large.
• Degree based network sampling was
proposed to reduce time complexity.
• Facebook (4039, 88234)
– {(17 min, 100%), (13 min, 70%), (8 min, 30%)}
Classification for Big data -- Binary
Classifier based on Conjunction Rule
• Naïve Bayes for Binary Data
• B1 ˄ B2 ˄ ┐B3 ˄ … ˄ BM => (C = 1)
• Contingency Table is used as the
sufficient statistic
Attribute
Class
Total
0
1
0
N00(j)
N01(j)
No
1
N10(j)
N11(j)
N1
Performance Improvement
• Compared to CART[2]
• Faster by a factor of log N
• For M = 500 and N = 30,000
• Time for CART = 3 minutes
• Time for BCC = 0.038 seconds
• Proposed algorithm is suitable for Data
Streams ---Just update the contingency
tables
[2] Denison, David GT, Bani K. Mallick, and Adrian FM Smith. "A bayesian CART algorithm." Biometrika 85.2
(1998): 363-377.
5
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Melissa A. Green
Mechanical and Aerospace Engineering
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
Coherent structures in fluid dynamics
Coherent structures in fluid dynamics
•
Bio-inspired propulsion
Coherent structures in fluid dynamics
•
Bio-inspired propulsion
•
Vortex-induced unsteady
forces
Coherent structures in fluid dynamics
•
Bio-inspired propulsion
•
Vortex-induced unsteady
forces
•
Turbulence
Coherent structures in fluid dynamics
•
Bio-inspired propulsion
•
Vortex-induced unsteady
forces
•
Turbulence
•
Turbulence + combustion
Coherent structures in fluid dynamics
•
Bio-inspired propulsion
•
Vortex-induced unsteady
forces
•
Turbulence
•
Turbulence + combustion
Lagrangian coherent structures
•
Coherent motions or patterns
in complex 3D unsteady fluids
•
Ridges of finite-time Lyapunov
exponent yield dynamically
relevant manifolds
Lagrangian coherent structures
•
Coherent motions or patterns
in complex 3D unsteady fluids
•
Ridges of finite-time Lyapunov
exponent yield dynamically
relevant manifolds
Lagrangian coherent structures
•
Coherent motions or patterns
in complex 3D unsteady fluids
•
Ridges of finite-time Lyapunov
exponent yield dynamically
relevant manifolds
Lagrangian coherent structures
•
Coherent motions or patterns
in complex 3D unsteady fluids
•
Ridges of finite-time Lyapunov
exponent yield dynamically
relevant manifolds
•
Intersections are critical points
Lagrangian coherent structures
•
Coherent motions or patterns
in complex 3D unsteady fluids
•
Ridges of finite-time Lyapunov
exponent yield dynamically
relevant manifolds
•
Intersections are critical points
•
Low-dimensional way to
track dynamically-relevant
structures in the complex
field
Lagrangian coherent structures
•
Coherent motions or patterns
in complex 3D unsteady fluids
•
Ridges of finite-time Lyapunov
exponent yield dynamically
relevant manifolds
y/h
•
Intersections are critical points
•
Low-dimensional way to
track dynamically-relevant
structures in the complex
field
U
•
Potential in fluid dynamics for flow control, drag
reduction, efficient propulsion
•
Tools could be useful in problems where dynamics
can be captured from differential equations or
trajectory tracking
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Embedded Computer Vision and Mobile Smart Camera
Applications
Monday,
APRIL 6, 2015
Senem Velipasalar
Electrical Engineering and Computer Science Department
Smart Vision Systems Laboratory
vision.syr.edu
Sheraton Syracuse University
Hotel & Conference Center
Wireless Embedded Smart Cameras
 A battery-powered, wireless embedded smart
camera is a stand-alone unit that not only captures
images, but also includes a processor, memory and
communication interface, and thus combines
sensing, processing and communication on a single
embedded platform.
 Application areas for smart cameras
include cyber-physical systems, video
surveillance
systems,
intelligent
transportation systems, UAVs, medical
applications, elder care, robotics,
wildlife monitoring, entertainment,
manufacturing and inspection.
 Battery-powered embedded smart cameras
introduce many additional challenges including:
Limited energy - 4AA batteries
Limited processing power and memory
Limited bandwidth
Smart Vision Systems Laboratory
Syracuse University
This work has been funded in part by NSF CAREER grant CNS1206291 and NSF Grant CNS-1302559.
2
Indoor Scene Understanding for Vision
Based Smart Autonomous UAVs
 Towards smart and autonomous
 LBP- Based Face Tracking:
 Object Detection
and Navigation:
 Face Tracking Video Demo:
UAVs for indoor applications
 Java + OpenCV based frame work
for computer vision applications
Smart Vision Systems Laboratory
Syracuse University
This work has been funded in part by NSF CAREER grant CNS1206291 and NSF Grant CNS-1302559.
3
Wearable Camera- and Accelerometer-based Fall
Detection on Portable Devices
 We developed a fall detection system using wearable
devices, e.g. smart phones and tablets, equipped with
cameras and accelerometers.
 Since the portable device is worn by the subject,
monitoring is not limited to confined areas, as
opposed to static sensors installed at certain rooms.
 Fusing camera and accelerometer data not only
increases the detection rate, but also significantly
decreases the number of false alarms.
 Experimental results and trials with actual Samsung
Galaxy phones show that combining two different
sensor modalities, provides much higher sensitivity,
and a significant decrease in the number of false
positives during daily activities.
Android smartphone attached to belt
Smart Vision Systems Laboratory
Syracuse University
This work has been funded in part by NSF CAREER grant CNS1206291 and NSF Grant CNS-1302559.
4
Energy-efficient Depth-Image Assisted Object
Detection on Smart Phones
 Object detection is computationally demanding,
and processing power and energy are limited
resources for mobile devices.
 We efficiently perform object detection by
incorporating depth image, and integrating
dynamic voltage frequency scaling (DVFS).
 The search region is narrowed down by
applying a threshold on depth image.
 A ground removal algorithm is applied to
further reduce the search regions.
 Haar feature-based object detection is
performed in the reduced area of RGB image.
 If an object of interest cannot be found, the
frequency of the processor is scaled down. If an
object of interest is detected, the frequency of
the processor is increased.
 Average energy savings can reach up to 38.90%.
Smart Vision Systems Laboratory
Syracuse University
This work has been funded in part by NSF CAREER grant CNS1206291 and NSF Grant CNS-1302559.
5
Other Research Activities
 Tracking of Vehicle Taillights and Alert Signal
Detection by Embedded Smart Cameras
We developed a robust and computationally lightweight algorithm for
a real-time vision system, capable of detecting and tracking vehicle
taillights, recognizing common alert signals using a vehicle-mounted
embedded smart camera, and counting the cars passing on both sides
of the vehicle. The system processes scenes entirely on the
microprocessor of an embedded smart camera.
 Mobile Standards-Based Traffic Light Detection
in Assistive Devices for Individuals with CVD
A robust, traffic-standards-based, and computationally efficient
method for detecting the status of the traffic lights without relying on
Global Positioning System, lidar, radar information, or prior (mapbased) knowledge. The system can accurately identify the status of the
light at 400 ft away from the intersection, reliably detecting solid,
faulty, arrow, and high-visibility signal lights. Over 50 h of video (over
2000 intersections) were tested with the system with 97.5% accuracy
of solid light detection.
 Scale Estimation with Difference of Ordered
Residuals
We developed a scale estimator and applied it
to multiple model estimation problems for
detecting planes and two-view motions.
Smart Vision Systems Laboratory
Syracuse University
This work has been funded in part by NSF CAREER grant CNS1206291 and NSF Grant CNS-1302559.
6
vision.syr.edu
http://eng-cs.syr.edu/faculty/velipasalar/
Smart Vision Systems Laboratory
Syracuse University
This work has been funded in part by NSF CAREER grant CNS1206291 and NSF Grant CNS-1302559.
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Nangia Research Group
Department of Biomedical and Chemical Engineering
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
Computational modeling of biological systems
Designing
anticancer
nanocarriers
Quantum
dots-protein
corona
Bacterial
membranes
Antimicrobial
peptides
Blood brain barrier
tight junctions
Cellular Uptake
of nanoparticles
Protein structure of
Ebola virus 2
Length and Time scale challenge
3
Current Solutions
• High Performance Computing
resources. Codes are tightly
coupled MPI & OpenMP
• Visualization –Images, animations,
trajectories that better represent
the phenomena
• Storage –Archiving volumes of data
that is readily available for retrieval
STAMPEDE CLUSTER
4
“Bigger data” ahead….
Variety
Particle-Positions/Momenta/
Accelerations/Forces/Pressure
Velocity
Femtosecond/Picosecond/Nano
second/microsecond/
millisecond/second/hour/days
Volume
7× 1027 atoms
Megabytes/gigabytes/terabytes
/petabytes/zetabytes
5
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Vir V Phoha
Department of Electrical Engineering & Computer Science
FACULTY BRIEFINGS
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
Attack
Defense
Research Area
Research
Manipulation of Learning
Systems– Medical Cyber
Physical Systems
Foundation Research and
algorithms
Breaching authentication
Video analytics; Robotic
attacks
Foundation Research and
algorithms
Prototype
Mobile biometric
authentication
Foundation Research and
algorithms
Prototype
DARPA
Prototype
DARPA
Desktop biometric
authentication
Technology Funding Source
White paper
accepted: Invited
Proposal by DHS
2
Adaptive systems that learn from examples
Attacks on Learning (adaptive) systems
Adaptive Implantables
• Rate adaptive pacemakers
• Implantable pulse generators
Adaptive control in cars
Web based systems
• Spam filters
• Intrusion Detection Systems
Mobile behavioral biometric authentication
3
Reasons that an
attack will work
Variation in
the signatures
Temporal drift
Overlap of
patterns over
large
populations
sets
4
Defense --DARPA TA1a and TA1b Active Authentication
TA1a: Desktop
Atomic
Keystroke
Features
(Enhanced)
Higher
Level
Keystroke
Features
TA1b: Mobile
Sensor Readings
Typing (T)
Swiping (S)
Cognilinguistic
Demographic
Accelerometer (A)
T+A
A S+A
A
S+A
Behavior Modeling and Fusion
Authentication, Feedback for Template Update
© Louisiana Tech University
Experiment Results
 (Desktop) Atomic Keystrokes: Accuracy 96.641%, FAR .0295, FRR 0.0382
Experiment:
(Desktop) 831 volunteer participants
(Mobile) 178 volunteers participants
The above results were obtained using typing data collected from volunteers in a single
application context. Preliminary work indicates that system accuracy with passively
collected data from multi-application, multi-window environments may initially be lower.
 (Mobile) Swiping: FAR: 10.3%, FRR: 10.3%
Typing: FAR: 19%, FRR: 19%
Body Movements (Gait): FAR: 7.9% , FRR: 7.9%
Typing + Accelerometer : FAR: 13.2%, FRR: 13.2%
5
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Yingbin Liang
Department of Electrical Engineering &
Computer Science
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference Center
Machine Learning of Big Data


Challenges:


Ultra high dimension: Machine
Learning data size grows
rapidly(GB, TB), up to billion
features.
Existing ML algorithms does
not scale well or even fail
Our Solutions:



Muti-task learning
Truncating large problem
Distributed and parallel
implementation
Study 1: Structure Learning of Large
Networks:
 Simultaneously learn multiple networks
 Sample size is significantly reduced
Study 2: Feature Selection in Human
Genome
 Truncate feature set
 Sequential and parallel processing
 Theoretical performance guarantee
Asynchronous ML Algorithm

Fully synchronized distributed
systems have communication
bottleneck
Asynchronous System
Avoids communication bottleneck
More efforts can be spent on
computational work
i = 1,..., k
c +1
i
x
k machines
c
= x + ∑ U i ( xit ) local fresh updates
0
i
k
+∑
j =1\ i
t =0
Tic, j
t
U
x
(
∑ j j ) delayed updates
t =0
c
0 ≤ c − Ti , j ≤ s
Strong Theoretical Gurantees for
2
l0 , l1 , groupl0 , groupl1 , x 2 regularized
non-convex Lasso Type problems:
Algorithm converges to critical
point with finite length
∞
∑
from other machines
bounded delay
O(1/c)
x c +1 − x c < +∞
c =0
rate in convex case
( ) ( )
F x c +1 − F x c ≤ O(1 / c )
Detection of Anomalous Patterns
Anomalous segment

Existing approaches detects only
based on individual quantities
Our problem: Anomalous patterns
x
x
x
x x
x x
x
distribution space
x

x
x x x
x
Sample sets
from different
distributions
o
o
o
oo
o o
o o
o
o
o
kernel
o
p
q

Hilbert space
Our approach: kernelbased method
MMD[p,q]

Nonparametric nature: no
prior information about
patterns is required
 Guaranteed performance
for arbitrary patterns
Dirty Interference Cancellation for
4G and LTE systems
Wireless Networks  Current
 Interference management is an

critical issue
Using orthogonal resources
Cellular User
D2D
Transmitter 2
Message
D2D
Receiver 2

Our new perspective

Base
Station
Simultaneous transmission
 Interferer deals with interference it causes
 Cancel large power interference with small additional
power
PHY-Based Secret Sharing and Secret
Key Generation

Traditional Approach


Number theory based technique
for secret key sharing
Trusted third party

Public
Discussion
Eve
Our Apporach


SK
PK

Exploit physical channel randomness
Flexible to be generated to multisecret sharing and multi-key
generation
Characterization of performance limits
over all possible approaches
NUNAN RESEARCH DAY
CYBER, WIRELESS and BIG DATA
Yuzhe Tang
Department of Electrical Engineering & Computer Science
Monday, APRIL 6, 2015
Sheraton Syracuse University Hotel & Conference
Center
Cloud computing and big data
The era of cloud computing has arrived:
Daily life changed by cloud computing
Many cloud service providers in the market
Human activities
Cloud service providers
2
Cloud Computing Barrier
• One hand, people reluctant to using Cloud.
–Security/lack of trust are top concerns.
3
Cloud Computing Barrier
• One hand, people reluctant to using Cloud.
–Security/lack of trust are top concerns.
•The other hand, cloud not trustworthy
–Caught being “evil” all the time
Cloud service provider
PRISM scandal!
4
My Research: Overcome the Barrier
My research is to bridge the gap by building
Secure & Trustworthy Distributed Systems
Two current focuses:
1. Trustworthy key-value stores
2. Efficient multi-party computations for federated
data analysis
5
Research Details
Trustworthy Key-Value Stores
-Verifiable data freshness & consistency
-NewSQL with predictable performance
Efficient Multi-Party Computations
-Scalable MPC
-Tunable MPC on heterogeneous hardware
6
Thank you.
Contact:
Yuzhe Tang
Assistant Professor
Syracuse University
[email protected]
http://ecs.syr.edu/faculty/yuzhe