trusted computing amidst untrustworthy intermediaries n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Trusted Computing Amidst Untrustworthy Intermediaries PowerPoint Presentation
Download Presentation
Trusted Computing Amidst Untrustworthy Intermediaries

Loading in 2 Seconds...

play fullscreen
1 / 24

Trusted Computing Amidst Untrustworthy Intermediaries - PowerPoint PPT Presentation


  • 121 Views
  • Uploaded on

Trusted Computing Amidst Untrustworthy Intermediaries. Mike Langston Department of Computer Science University of Tennessee currently on leave to Computer Science and Mathematics Division Oak Ridge National Laboratory USA. Overview. Highly Parallel Scalable Network Variable Topology

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Trusted Computing Amidst Untrustworthy Intermediaries' - ishi


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
trusted computing amidst untrustworthy intermediaries
Trusted Computing Amidst Untrustworthy Intermediaries

Mike Langston

Department of Computer Science

University of Tennessee

currently on leave to

Computer Science and Mathematics Division

Oak Ridge National Laboratory

USA

slide2

Overview

Highly Parallel

Scalable Network

Variable Topology

Internet Like

But Untrusted!

Programs

Data

slide3

Possible Solutions

  • Accept faulty results.

Uh, no thanks.

  • Authenticate/verify by central authority.

Unrealistic, does not scale.

  • Exploit complexity and checkability.

Problems in NP can be hard to solve -- but they are

always easy to check!

No need for centralized control, ownership,

or verification.

slide4

A Little Complexity Theory

The Classic View:

“easy”

P

NP

Σ

P

PSPACE

2

slide5

A Little Complexity Theory

  • The Classic View:

“easy”

NP-complete

P

NP

Σ

P

PSPACE

2

“hard”

slide6

A Little Complexity Theory

  • The Classic View:

“fuggettaboutit”

“easy”

P

NP

Σ

P

PSPACE

2

“hard”

slide7

Parameter Sensitivity: Instance(n,k)

  • Suppose our problem is, say, NP-complete.
  • Consider an algorithm with a time bound such as O(2k+n).
  • And now one with a time bound more like O(2k+n).
slide8

Parameter Sensitivity: Instance(n,k)

  • Suppose our problem is, say, NP-complete.
  • Consider an algorithm with a time bound such as O(2k+n).
  • And now one with a time bound more like O(2k+n).
  • Both are exponential in parameter value(s).
slide9

Parameter Sensitivity: Instance(n,k)

  • Suppose our problem is, say, NP-complete.
  • Consider an algorithm with a time bound such as O(2k+n).
  • And now one with a time bound more like O(2k+n).
  • Both are exponential in parameter value(s).
  • But what happens when k is fixed?
slide10

Parameter Sensitivity: Instance(n,k)

  • Suppose our problem is, say, NP-complete.
  • Consider an algorithm with a time bound such as O(2k+n).
  • And now one with a time bound more like O(2k+n).
  • Both are exponential in parameter value(s).
  • But what happens when k is fixed?
  • Fixed Parameter Tractability: confines superpolynomial behavior to the parameter.
slide11

Complexity Theory, Revised

Hence, the Parameterized View:

“solvable

(even if

NP-complete)”

W[2]

XP

W[1]

FPT

slide12

Complexity Theory, Revised

The Parameterized View:

“solvable

(even if

NP-hard!)”

W[2]

XP

W[1]

FPT

“heuristics only”

slide13

Complexity Theory, Revised

The Parameterized View:

“I said fuggettaboutit!”

“solvable

(even if

NP-hard!)”

W[2]

XP

W[1]

FPT

“heuristics only”

target problems
Target Problems
  • Not membership in P (assuming P≠NP)
    • hard to compute
target problems1
Target Problems
  • Not membership in P (assuming P≠NP)
    • hard to compute
  • Membership in NP
    • easy to check
target problems2

NP-complete

FPT

Target Problems
  • Not membership in P (assuming P≠NP)
    • hard to compute
  • Membership in NP
    • easy to check
  • Fixed Parameter Tractable
    • use kernelization and branching
kernelization
Kernelization
  • Consider Clique and Vertex Cover
  • High Degree Rule(s)
  • Low Degree Rule(s)
  • LP, Crown Reductions
    • kernel of linear size, and extreme density
    • the “hard part” of the problem instance
branching
Branching
  • Let’s stay with Clique and Vertex Cover
  • Bounded tree search
  • Depth at most k
  • With this technique, we can now solve vertex cover in O(1.28k+n) time
  • Easily parallelizable
  • No processor sees another’s work, nor the original graph
slide19

Branching as

A Form of

Cyber Security

Data

decomposition

Answer check

(NP certificate)

.

Untrusted intermediaries

cannot deduce data

Nor can they

spoof answers

. . . . . .

overall appeal
Overall Appeal
  • Verifiability
    • easy to check answers: a faulty or malicious processor cannot invalidate or subvert computations
overall appeal1
Overall Appeal
  • Verifiability
    • easy to check answers: a faulty or malicious processor cannot invalidate or subvert computations
  • Security
    • damage from intrusion contained: strong concealment of the total problem is a natural part of this method
overall appeal2
Overall Appeal
  • Verifiability
    • easy to check answers: a faulty or malicious processor cannot invalidate or subvert computations
  • Security
    • damage from intrusion contained: strong concealment of the total problem is a natural part of this method
  • Scalability
    • branching translates into partitioning: no a priori bounds on the degree of parallelism
overall appeal3
Overall Appeal
  • Verifiability
    • easy to check answers: a faulty or malicious processor cannot invalidate or subvert computations
  • Security
    • damage from intrusion contained: strong concealment of the total problem is a natural part of this method
  • Scalability
    • branching translates into partitioning: no a priori bounds on the degree of parallelism
  • Robustness
    • subtrees are compartmentalized: processes can be reassigned at will
research thrusts
Research Thrusts
  • Range of amenable problems?
    • FPT
    • non FPT
  • Ubiquity of untrustworthy processors?
    • grid computing
    • unbrokered resource sharing
  • Relationship to traditional forms of security?
    • internet-style lightweight security
    • no heavyweight authentication needed