Trust based Clustering for Group Trust based Clustering for Group - - PowerPoint PPT Presentation

▶
trust based clustering for group trust based clustering
SMART_READER_LITE
LIVE PREVIEW

Trust based Clustering for Group Trust based Clustering for Group - - PowerPoint PPT Presentation

Jan 19, 2023 251 likes •421 views

Trust based Clustering for Group Trust based Clustering for Group Trust based Clustering for Group Trust based Clustering for Group Key Management Key Management Key Management Key Management Hamida SEBA Graphs, Algorithms and Applications

slide-1
SLIDE 1

Laboratoire d'InfoRmatique en Image et Systèmes d'information

LIRIS UMR 5205 CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/Université Lumière Lyon 2/Ecole Centrale de Lyon

http://liris.cnrs.fr

Trust based Clustering for Group Trust based Clustering for Group Key Management Key Management Trust based Clustering for Group Trust based Clustering for Group Key Management Key Management

Hamida SEBA

Graphs, Algorithms and Applications

slide-2
SLIDE 2

Hamida SEBA

2

In this talk: In this talk:

 Group based applications  Security of Group communication  Group key management  Trust based clustering for group key management

slide-3
SLIDE 3

Hamida SEBA

Group based applications Group based applications

3

  • Teleconferencing
  • Collaborative work
  • Replicated databases
  • Distributed interactive simulation
  • E-learning
  • Etc.

Process Web service Agent End user application Etc.

slide-4
SLIDE 4

Hamida SEBA

4

Security of Group Communication Security of Group Communication

Prevention Confidentiality Authentication Integrity Non-repudiation

slide-5
SLIDE 5

5

Hamida SEBA

Confidentiality Confidentiality- Key Management Key Management

 Solution= Encryption

  • Symmetric Key : shared between the sender and the receivers.
  • This key is called : the group key

Main issue : how to compute and distribute keys?

slide-6
SLIDE 6

Hamida SEBA

GROUP KEY MANAGEMENT GROUP KEY MANAGEMENT

 GROUP KEY: a secret quantity known only to current group members  BACKWARD SECRECY

  • Any subset of group keys cannot be used to discover previous group

keys

 FORWARD SECRECY

  • Any subset of group keys cannot be used to discover subsequent keys

 THE GROUP KEY MANAGEMENT PROTOCOL MUST UPDATE THE GROUP KEY (REKEY)

A new group member can not read data exchanged before he joins the group An excluded member can not read data exchanged after he leaves the group 6

slide-7
SLIDE 7

Hamida SEBA

MODELS OF GROUP KEYS (1) MODELS OF GROUP KEYS (1)

 GROUP KEY DISTRIBUTION

  • One party generates a secret key and distributes it to others

Group member No key generation Group member Does key generation Key node Pairwise model Hierarchical model (tree of members or third parties

slide-8
SLIDE 8

Hamida SEBA

MODELS OF GROUP KEYS (2) MODELS OF GROUP KEYS (2)

 GROUP KEY AGREEMENT

  • Secret key is derived jointly by two or more parties
  • Key is a function of information contributed by each member
  • No party can pre-determine the result

Group member Does key generation Distributed Tree of keys (maintained by each member) No pre-determined structure Key node

slide-9
SLIDE 9

Hamida SEBA

9

Group Key computation: an example

Protocole de Perrig et al.,2000

2 , 1

bk

4 , 3

bk

p g clé

N N N N

g g

mod

4 3 2 1

 p g k

N N

mod

2 1

2 , 1 

p g k

N N

mod

4 3

4 , 3 

N1 N2 N3 N4

p g N b

i

N i

mod 

1

bN

2

bN

3

bN

4

bN

M1 M2 M3 M4

slide-10
SLIDE 10

Hamida SEBA

MODELS OF GROUP KEYS (3) MODELS OF GROUP KEYS (3)

 Hybrid Solutions

  • Cluster based.

Group member Does key generation Simple node

Key Distribution Key Agreement

  • How to construct/maintain

clusters?

  • How to compute inter-cluster

keys and intra-cluster keys? How about a security based clustering?

slide-11
SLIDE 11

Hamida SEBA

11 Good interaction Bad interaction

Trust Trust-based Clustering based Clustering

Know each other: Establish trust/distrust relations

Application dependent Peer to peer network: Nodes: promiscuous mode Forward packet: + Black hole attack: - Recommendations, etc.

  • Log and analyze

interactions

  • Give scores
slide-12
SLIDE 12

Hamida SEBA

12

Trust Trust-based Clustering based Clustering

 Two trust thresholds : Smax and Smin

[1, SminSmax,1]

  • Total trust (TT)
  • tv(

tv(i,j i,j) and tv( ) and tv(j,i j,i) ) Є Є [Smax

max,1]

,1]

  • Partiel Trust (PT)
  • tv(

tv(i,j i,j) ) Є Є [Smax

max,1] and

,1] and tv( tv(j,i j,i) ) Є Є [Smin

min,

, Smax

max]

  • tv(

tv(i,j i,j) ) Є Є [Smin

min,

, Smax

max] and

] and tv( tv(j,i j,i) ) Є Є [Smax

max,1]

,1]

  • tv(

tv(i,j i,j) ) and and tv( tv(j,i j,i) ) Є Є [Smin

min,

, Smax

max]

  • Distrust (DT)
  • tv(

tv(i,j i,j) ) and and tv( tv(j,i j,i) ) Є Є [-1, 1, Smin

min] i j k tv(i,j) tv(i,k) tv(k,i) tv(j,i) l tv(i,l) tv(l,i)

slide-13
SLIDE 13

Hamida SEBA

13 13

Trust Trust-based Clustering based Clustering

Two trust thresholds : Smax and Smin

  • Total trust (TT)
  • tv(

tv(i,j i,j) and tv( ) and tv(j,i j,i) ) Є Є [Smax

max,1]

,1]

  • Partiel Trust (PT)
  • tv(

tv(i,j i,j) ) Є Є [Smax

max,1] and

,1] and tv( tv(j,i j,i) ) Є Є [Smin

min,

, Smax

max]

  • tv(

tv(i,j i,j) ) Є Є [Smin

min,

, Smax

max] and

] and tv( tv(j,i j,i) ) Є Є [Smax

max,1]

,1]

  • tv(

tv(i,j i,j) ) and and tv( tv(j,i j,i) ) Є Є [Smin

min,

, Smax

max]

  • Distrust (DT)
  • tv(

tv(i,j i,j) ) and and tv( tv(j,i j,i) ) Є Є [-1, 1, Smin

min]

i j k l TT PT DT

slide-14
SLIDE 14

Hamida SEBA

Clusterheads Max number of TT relations

Trust Trust-based Clustering based Clustering

Distrusted node

Cluster periphery (key distribution zone) Cluster core (TT) (key agreement zone)

Self-stabilizing algorithm:

  • Adaptive
  • Self-maintaining
slide-15
SLIDE 15

15

Hamida SEBA

15

1 3 1 2 2 2 3 3 3 1 1 1 1 1 2 1 3 3 3 1 1 1 1 2 1 3 2 2

Example Example