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![Page 1: Fun with Networks: Social, Sensor, and Shape-Shifting Phillip B. Gibbons Intel Research Pittsburgh DISC’08 / Graal’08 September 24, 2008 Slides (except.](https://reader038.fdocuments.us/reader038/viewer/2022103122/56649cef5503460f949bd691/html5/thumbnails/1.jpg)
Fun with Networks:Social, Sensor, and Shape-Shifting
Phillip B. GibbonsIntel Research Pittsburgh
DISC’08 / Graal’08September 24, 2008
Slides (except those borrowed from colleagues) are © Phillip B. Gibbons
![Page 2: Fun with Networks: Social, Sensor, and Shape-Shifting Phillip B. Gibbons Intel Research Pittsburgh DISC’08 / Graal’08 September 24, 2008 Slides (except.](https://reader038.fdocuments.us/reader038/viewer/2022103122/56649cef5503460f949bd691/html5/thumbnails/2.jpg)
Phillip B. Gibbons, DISC’08/Graal’083
Fun with Networks
Social Networks– SybilLimit: Defending against Sybil Attacks in P2P
Sensor Networks– Synopsis Diffusion: Robust in-network aggregation
Shape-Shifting Networks– Claytronics: Aggregation in programmable matter
![Page 3: Fun with Networks: Social, Sensor, and Shape-Shifting Phillip B. Gibbons Intel Research Pittsburgh DISC’08 / Graal’08 September 24, 2008 Slides (except.](https://reader038.fdocuments.us/reader038/viewer/2022103122/56649cef5503460f949bd691/html5/thumbnails/3.jpg)
Phillip B. Gibbons, DISC’08/Graal’084
Background: Sybil Attack
Sybil attack:Single user assumes many fake/sybil identities– Already observed in real-world
p2p systems
Sybil identities can become a large fractionof all identities – “Out-vote” honest users in
collaborative tasks
launchsybilattack
honest
malicious
![Page 4: Fun with Networks: Social, Sensor, and Shape-Shifting Phillip B. Gibbons Intel Research Pittsburgh DISC’08 / Graal’08 September 24, 2008 Slides (except.](https://reader038.fdocuments.us/reader038/viewer/2022103122/56649cef5503460f949bd691/html5/thumbnails/4.jpg)
Phillip B. Gibbons, DISC’08/Graal’085
Background:Defending Against Sybil Attack
Using trusted central authority (TCA) – Ties identities to human beings
– Not always desirable: who to trust, privacy, etc.– Practice: Gmail accounts
Much harder without a TCA [Douceur’02]– Resource challenges not sufficient
– IP address-based approach not sufficient– Practice: Wikipedia IP blocking
Widely considered real & challenging– 40 papers on sybil attacks, no distributed solution
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Phillip B. Gibbons, DISC’08/Graal’086
SybilGuard/SybilLimit Basic Insight: Leveraging Social Networks
Nodes = identities Undirected edges =
strong mutual trust – E.g., colleagues,
relatives in real-world
– Not online friends !
SybilGuard [SIGCOMM’06, TON 2008], SybilLimit [Oakland’08]
(with Haifeng Yu*, Michael Kaminsky)
First to leverage social networks for thwarting sybil attacks with provable guarantees
* Primary author
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Phillip B. Gibbons, DISC’08/Graal’087
Attack Model
malicioususers
honestnodes
Observation: Adversary cannot create extra attack edges
attack edges
n honest users: One identity/node each Malicious users: Multiple identities each (sybil nodes)
sybil nodes
sybil nodes may collude – the adversary
Attack edge: edge between honest node& sybil node
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Phillip B. Gibbons, DISC’08/Graal’088
SybilGuard/SybilLimit Basic Insight
honest nodes sybil nodes
Dis-proportionally small cut disconnecting a large number of identities
But cannot search brute-force…attack
edges
![Page 8: Fun with Networks: Social, Sensor, and Shape-Shifting Phillip B. Gibbons Intel Research Pittsburgh DISC’08 / Graal’08 September 24, 2008 Slides (except.](https://reader038.fdocuments.us/reader038/viewer/2022103122/56649cef5503460f949bd691/html5/thumbnails/8.jpg)
Phillip B. Gibbons, DISC’08/Graal’089
SybilLimit End Guarantees
Completely decentralized
Enables any given verifier node to decide whether to accept any given suspect node– Accept: Provide service to / receive service from
– Ideally: Accept and only accept honest nodes – unfortunately not possible
Bounds # of accepted sybil nodes (w.h.p.)
Accepts (1- )n honest nodes (w.h.p.)
nnO log/ per attack edge [up to)(logn attack edges]
We also prove that SybilLimit is away from optimal)(lognO
![Page 9: Fun with Networks: Social, Sensor, and Shape-Shifting Phillip B. Gibbons Intel Research Pittsburgh DISC’08 / Graal’08 September 24, 2008 Slides (except.](https://reader038.fdocuments.us/reader038/viewer/2022103122/56649cef5503460f949bd691/html5/thumbnails/9.jpg)
Phillip B. Gibbons, DISC’08/Graal’0810
Example Application Scenarios
If # of sybil nodes accepted is
Then applications can do
< n/2 byzantine consensus
< n majority voting
< n/c for some constant c secure DHT [Awerbuch’06,
Castro’02, Fiat’05]
… …
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Phillip B. Gibbons, DISC’08/Graal’0811
Identity Registration
Each node (honest or sybil) has a locally generated public/private key pair– “Identity”: V accepts S means
V accepts S’s public key KS– We do not assume/need PKI
Every suspect S “registers” KS on some other nodes
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Phillip B. Gibbons, DISC’08/Graal’0812
Registration Goals
Ensure that sybil nodes (collectively) register only on limited number of honest nodes
– Still provide enough “registration opportunities” for honest nodes
sybil regionhonest region
K: registered keys of sybil nodes
K K
K
KK
K
K K
K
K
K
K
K
KK K
K: registered keys of honest nodes
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Phillip B. Gibbons, DISC’08/Graal’0813
Acceptance Criteria
Accept S only if KS is register on sufficiently many honest nodes
– Without knowing where the honest region is !
– Circular design? We can use small cut against adversary
K K
K
KK
K
K K
K
K
K
K
K
KK K
sybil regionhonest region
K: registered keys of sybil nodes
K: registered keys of honest nodes
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Phillip B. Gibbons, DISC’08/Graal’0814
)(logn Take random “walks” of w= hops– Honest nodes: likely to remain in honest region*
– Sybil nodes: must cross an attack edge to reach honest region
Key Idea
sybil regionhonest region
K K
K
KK
K
K K
K
K
K
K
K
KK K
• Register key at last hop of “walk”
* w = Social network’s mixing time End up at ~random edge in honest region
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Phillip B. Gibbons, DISC’08/Graal’0815
Random 1 to 1 mapping between incoming edge and outgoing edge
Random Route: Convergence
a db ac bd c
d ee df f
a
b
c
d e
f
randomized
routing table
Using routing table gives Convergence Property:
Routes merge if crossing the same edge
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Phillip B. Gibbons, DISC’08/Graal’0816
Implication of Convergence
Claim: There are at most w K’s per attack edge– Proof: By the Convergence property
– Regardless of whether sybil nodes follow protocol
honestnodes
sybilnodes
attack edgeK
K KK
Route length w
Use independent instances of random routing m
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Phillip B. Gibbons, DISC’08/Graal’0817
4. Is KS registered?
Verification Procedure
VS
1. request S’s set of tails AB
CDEF
F
2. I have three tails
AB; CD; EF
3.common tail: EF
5. Yes. 4 messages involved
V accepts S Tails intersect + key registered
Earlier: Each node registers at tails m
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Phillip B. Gibbons, DISC’08/Graal’0818
Further Details in Paper
Birthday paradox V & honest S share a common tail w.h.p.
Limit on sybil Ks in honest region V &sybil S don’t share a common tail w.h.p.– Unless V has a tail in sybil region: Handled in paper
How to estimate parameters: w & m
Evaluation w/ real-world social networks– Friendster, LiveJournal, DBLP (Added sybil nodes)
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Phillip B. Gibbons, DISC’08/Graal’0819
Conclusions (to Part I)
Sybil attack:– Widely considered a real & challenging problem
SybilLimit: Fully decentralized defense protocol based on social networks– Provable near-optimal guarantees
– Experimental validation on real social networks
Open Problem (in SybilLimit & Politics):
Honest users not voting
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Phillip B. Gibbons, DISC’08/Graal’0820
Fun with Networks
Social Networks– SybilLimit: Defending against Sybil Attacks in P2P
Sensor Networks– Synopsis Diffusion: Robust in-network aggregation
Shape-Shifting Networks– Claytronics: Aggregation in programmable matter
![Page 20: Fun with Networks: Social, Sensor, and Shape-Shifting Phillip B. Gibbons Intel Research Pittsburgh DISC’08 / Graal’08 September 24, 2008 Slides (except.](https://reader038.fdocuments.us/reader038/viewer/2022103122/56649cef5503460f949bd691/html5/thumbnails/20.jpg)
Phillip B. Gibbons, DISC’08/Graal’0821
Wireless Sensor Network Aggregation
Aggregate in-network over a tree– Each node sends 1 short message (saves energy)
10
0
10
20
30
40
50
60
70
0 10 20 30 40 50
Time
% N
od
es In
clu
ded
3
1 1
31
1
37
1
2 1
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Phillip B. Gibbons, DISC’08/Graal’0822
The Problem and the Goal
Tree topology used to avoid double-counting
Aggregation and routing are tightly coupled
Our goal: Decouple the two components– They can be independently optimized
– Robust multi-path routing can be used
– Can exploit the broadcast medium
11 1
1
3 13
7
1 1
3 45
12
In contrast, a gossip approach requires point-to-point messages & explicit acks
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Phillip B. Gibbons, DISC’08/Graal’0823
Synopsis Diffusion
Each node generates a small synopsis of its readings (SG)
Starting with outer ring, each node broadcasts its synopsis
Synopsis Fusion (SF): Each node in next ring combines all synopses it hears into its own synopsis
SF must be order- and duplicate- insensitive (ODI)
ExampleTopology:
Rings
e.g., Compute count or sum using Flajolet-Martin’s e.g., Compute count or sum using Flajolet-Martin’s distinct-values countingdistinct-values counting [Considine et al, ICDE’04] [Considine et al, ICDE’04]
[with Suman Nath*, Srini Seshan, Zach Anderson, SenSys’04, TOSN 2008]
* Primary author
![Page 23: Fun with Networks: Social, Sensor, and Shape-Shifting Phillip B. Gibbons Intel Research Pittsburgh DISC’08 / Graal’08 September 24, 2008 Slides (except.](https://reader038.fdocuments.us/reader038/viewer/2022103122/56649cef5503460f949bd691/html5/thumbnails/23.jpg)
Phillip B. Gibbons, DISC’08/Graal’0824
SD Example: Uniform Sample of Size K
SG(): Each node selects a random r in [0,1], and creates a synopsis (r, id, val)
SF(s,s’): Output the K (r,id,val) triples from s U s’ with maximum r-values
SE(s): Output the K val’s in s
K=2: (.4,1,v1), (.7,2,v2), (.3,3,v3), (.8,4,v4)
{(.4,1,v1),(.7,2,v2)}
{(.7,2,v2), (.4,1,v1)}
{(.7,2,v2),(.8,4,v4)}{v2,v4}
{(.7,2,v2),(.3,3,v3)}{(.3,3,v3),(.8,4,v4)}
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Phillip B. Gibbons, DISC’08/Graal’0825
Key Challenge & A Solution
ODI Goal: S1 is always the same
SF SF SF
SG
r1
SG
r2
SG
r3
SG
r4
SG
r5
SFSF SF
SF
SE
S1
Result
Aggregation Topology
SF SF SF
SFSF SF
SF
SF SF SF
SFSF SF
SF
SF SF SF
SFSF SF
SF
Potentially large unknown
set of combinations!
Key Result:Give 4 simple,locally testableproperties for ODI correctness(necessary & sufficient)
Makes topologyindependence tractable
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Phillip B. Gibbons, DISC’08/Graal’0826
Order- & Duplicate-Insensitive Synopses
Necessary & sufficient conditions1. SF is commutative
2. SF is associative
3. SF is same-synopsis idempotent: SF(s,s) = s
4. If readings r and r’ are “duplicates”, then SG(r) = SG(r’)
E.g., suppose use SF(s1,s2) = (s1+s2)/2, which of P1-P3 fails?
P2: SF(2,SF(6,30)) = 10 but SF(SF(2,6),30) = 17
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Phillip B. Gibbons, DISC’08/Graal’0827
Implications SF forms a semi-lattice Lattice property can tell if another ODI synopsis accounts for my synopsis
E.g., SF is bitwise-OR00101
10111
Implicit acks (Listen to what parent sends to know if your message was “received”)
Efficient adaptation to dynamic message loss, even when asymmetric links
More robust routing More accurate answers
4
6Not true for
non-ODI e.g., sum
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Phillip B. Gibbons, DISC’08/Graal’0828
ODI-Correct Algorithms
Count, Count Distinct, Sum, Average, Standard deviation, Second moment, Uniform sample, k’th statistical moment, Quantiles, Frequent items, Range aggregates, Inner product queries
For ODI-correct algorithms:Approximation guarantees
Well-studiedStreaming Model
=same3
52
2
2253
…
…
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Phillip B. Gibbons, DISC’08/Graal’0829
Synopsis Diffusion on Rings
Scheme EnergyTree (TAG) 41.8mjA. Rings 42.1mjFlood 685mj
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Loss Rate
RM
S E
rro
r
TAG Rings
Adaptive Rings Flood
More robust than TAGAlmost as energy efficient as TAG
600 sensors in 20x20Count query(tree)
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Phillip B. Gibbons, DISC’08/Graal’0830
Synopsis Diffusion vs. Tree
Tributary-Delta: run both simultaneously, depending on:
[with Amit Manjhi, Suman Nath, ICDE’05]
SD
Tree
Communication error
1%
60%
Approximationerror
10-15%
0-5%
Number of Packets
1-3
1
Delta
Tributary
• regional loss rate• accumulated aggregation
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Phillip B. Gibbons, DISC’08/Graal’0831
Conclusions (to Part II)
Synopsis Diffusion– ODI-correct algorithms + any multi-path routing
Open Problems– ODI-correct subtraction
– Use Synopsis Diffusion in other contexts:
– P2P, mobile, etc.
– ODI-correctness requires the same synopsis for all aggregation topologies
– However, too strong: E.g., quantiles – always meets guarantees but answer depends on order
– What is a formal framework for such scenarios?
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Phillip B. Gibbons, DISC’08/Graal’0832
Fun with Networks
Social Networks– SybilLimit: Defending against Sybil Attacks in P2P
Sensor Networks– Synopsis Diffusion: Robust in-network aggregation
Shape-Shifting Networks– Claytronics: Aggregation in programmable matter
![Page 32: Fun with Networks: Social, Sensor, and Shape-Shifting Phillip B. Gibbons Intel Research Pittsburgh DISC’08 / Graal’08 September 24, 2008 Slides (except.](https://reader038.fdocuments.us/reader038/viewer/2022103122/56649cef5503460f949bd691/html5/thumbnails/32.jpg)
Phillip B. Gibbons, DISC’08/Graal’0833
The Vision: A Material That Changes Shape
Large groups of tiny robot modules (106 -109 units), working in unison to form tangible, moving 3D shapes
Not just an illusion of 3D (as with stereo glasses), but real physical objects Both an output device (rendering, haptics) & an input device (sensing)
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Phillip B. Gibbons, DISC’08/Graal’0834
Suppose Software CouldControl Shape
Video: CMU Entertainment Technology Center
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Phillip B. Gibbons, DISC’08/Graal’0835
Applications
Product design Medical visualization Adaptive form-factor devices Telepario 3D fax Smart antennas Paramedic-on-demand Entertainment Etc.
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Phillip B. Gibbons, DISC’08/Graal’0836
Claytronics[PIs: Seth Goldstein, Jason Campbell, Todd Mowry]
Each sub-millimeter module (“catom”) integrates computing & actuation
Key issues: – very high concurrency (106 -109 catoms)– nondeterminism & unreliability– efficient actuators, strong adhesion– power, heat, dirt– complex, dynamic networking (network diameters
≥ 1000, and changing topologies)
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Phillip B. Gibbons, DISC’08/Graal’0837
Moving Catoms Without Moving Parts:
Two Potential Actuation Methods Magnetic field
Electric field
one coil two assembled magnet rings 2 magnetic-field prototype catoms
electrostatic latch design
completed latch
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Phillip B. Gibbons, DISC’08/Graal’0838
patterned “flower”,including actuators& control circuitry
arms curl up due to stresses between layers
Making Submillimeter Catoms
[J. Robert Reid, Air Force Research Labs]
[Igal Chertkow & Boaz Weinfeld, Intel]
2 mold wafersbonded around
1 thinned logic wafer
Note: Both areearly attempts
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Phillip B. Gibbons, DISC’08/Graal’0839
Catom Design Actuation: Roll across each other (using electrostatics) under software control– Planned motion, Reactive motion
Power: Form own power grid– Connected to external power source
Communication: Between physically adjacent modules– Either electrical contact, capacitive-coupled
connections, or free space optics (wire-like)
– Simultaneously with multiple neighbors
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Phillip B. Gibbons, DISC’08/Graal’0840
Aggregation Goal
In order to self-organize into a desired shape, the catom ensemble must:– Be able to measure key aggregate properties
(e.g., center of mass)
– Coordinate their activities
…in real time
Diameter too large for standard hop-by-hop approach
Ensemble too dense for longer range wireless
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Phillip B. Gibbons, DISC’08/Graal’0841
Speculative Forwarding[with Casey Helfrich, Todd Mowry, Babu Pillai,
Ben Rister, Srini Seshan]
Standard approach:(regular) gradient
E.g., regular 2D grid
Our approach:• Hierarchical Overlay• Speculative forwarding on the long links
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Phillip B. Gibbons, DISC’08/Graal’0842
Speculative Forwarding Each catom maintains incoming-to-outgoing link mapping (e.g., last used)
Each bit along incoming wire sent on outgoing wire according to the mapping
When accumulate header, check for miss-speculation
Aggregation deferred to nodes in the overlay
Many issues:• miss-speculations• creating overlay• shape changes
Initial resultsare promising
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Phillip B. Gibbons, DISC’08/Graal’0843
Conclusions (to Part III)
Shape-Shifting Networks pose a new problem domain for algorithmic research– Details are in flux; realizations years away
– Key issues: scale, dynamics, soft real-time
Open Problems– Much theory work to be done:
Formal modeling, new algorithms, new insights, lower bounds, etc.
– E.g., what is a robust, low-latency communication/aggregation scheme for catom ensembles?
– Ensemble algorithmics: local algsBrownian hole motionGrow/consume holes
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Phillip B. Gibbons, DISC’08/Graal’0844
Fun with Networks
Social Networks– SybilLimit: Defending against Sybil Attacks in P2P
Sensor Networks– Synopsis Diffusion: Robust in-network aggregation
Shape-Shifting Networks– Claytronics: Aggregation in programmable matter