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TheRisksofKeyRecovery,KeyEscrow,andTrustedThird-PartyEncryptionHalAbelson

1RossAnderson

2StevenM.Bello

vin3JoshBenaloh

4MattBlaze

5

6JohnGilmore

7PeterG.Neumann

8RonaldL.Riv

est9I.Sc

hiller10BruceSc

hneier11FinalReport

{27May199712AbstractAvarietyof\keyrecovery,”\keyescrow,”and\trustedthird-party”encryptionrequire-mentshavebeen

suggestedin

recentyearsb

ygovernmentagenciesseeking

toconductcovertsurveillancewithinthec

hangingen

vironmentsbrough

taboutb

ynewtec

hnologies.Thisreport

examinesthefundamen

talpropertiesoftheserequiremen

tsandattemptstooutlinethetec

hnicalrisks,costs,andimplicationsofdeplo

yingsystemsthatpro

vidego

vernmentaccesstoencryption

keys.1MITLaboratoryforComputerScience/Hewlett-P

ackard,<hal@mit.edu>2UniversityofCam

bridge,<ross.anderson@cl.cam.ac.uk>3AT&TLaboratories{Researc

h,<smb@research.att.com>4MicrosoftResearc

h,<benaloh@microsoft.com>5AT&TLaboratories{Researc

h,<mab@research.att.com>6SunMicrosystems,

<diffie@eng.sun.com>7<gnu@toad.com>8SRIIn

ternational,<neumann@sri.com>9MITLaboratoryforComputerScience,

<rivest@lcs.mit.edu>10MITInformationSystems,

<jis@mit.edu>11CounterpaneSystems,

<schneier@counterpane.com>12Thelatestv

ersionofthisdocumen

tcanbefoundonthew

orld-wide-webat

,inPostScriptformatat
study.ps>andinASCII

textformatat

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Plotting PDF files stored in ProjectWise

KIP Request and KIP Print applications cannot navigate to files stored within ProjectWise. To plot hard

copies of PDF files generated from either MicroStation V7 or V8i, you will use ProjectWise InterPlot

Organizer.

You m

ay also use Adobe Reader to plo

t single sheets. Spec

ial instructions for

plotting in

Adobe can be found at the end of this document.

From the Start menu, o

pen

ProjectWise InterPlot Organizer

by going to All

Programs>Bentley>ProjectWise Interplot Utilities.

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?, 2017 | ?1

enr.com/SpecialAd

Building for the Future

ACE Mentor Program

Yearbook 2018

SPECIAL ADVERTISING SECTION

AENR

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ResilientDistributedDatasets:AFault-TolerantAbstractionfor

In-MemoryClusterComputing

MateiZaharia,MosharafChowdhury,TathagataDas,AnkurDave,JustinMa,

MurphyMcCauley,MichaelJ.Franklin,ScottShenker,IonStoica

UniversityofCalifornia,Berkeley

Abstract

WepresentResilientDistributedDatasets(RDDs),adis-

tributedmemoryabstractionthatletsprogrammersper-

formin-memorycomputationsonlargeclustersina

fault-tolerantmanner.RDDsaremotivatedbytwotypes

ofapplicationsthatcurrentcomputingframeworkshan-

dleinefiterativealgorithmsandinteractivedata

miningtools.Inbothcases,keepingdatainmemory

canimproveperformancebyanorderofmagnitude.

Toachievefaulttoleranceef,RDDsprovidea

restrictedformofsharedmemory,basedoncoarse-

grainedtransformationsratherthanupdates

tosharedstate.However,weshowthatRDDsareexpres-

siveenoughtocaptureawideclassofcomputations,in-

cludingrecentspecializedprogrammingmodelsforiter-

ativejobs,suchasPregel,andnewapplicationsthatthese

modelsdonotcapture.WehaveimplementedRDDsina

systemcalledSpark,whichweevaluatethroughavariety

ofuserapplicationsandbenchmarks.

1Introduction

ClustercomputingframeworkslikeMapReduce[10]and

Dryad[19]havebeenwidelyadoptedforlarge-scaledata

analytics.Thesesystemsletuserswriteparallelcompu-

tationsusingasetofhigh-leveloperators,withouthaving

toworryaboutworkdistributionandfaulttolerance.

Althoughcurrentframeworksprovidenumerousab-

stractionsforaccessingacluster’scomputationalre-

sources,theylackabstractionsforleveragingdistributed

memory.Thismakestheminefforanimportant

classofemergingapplications:thosethat

reuse

interme-

diateresultsacrossmultiplecomputations.Datareuseis

commoninmany

iterative

machinelearningandgraph

algorithms,includingPageRank,K-meansclustering,

andlogisticregression.Anothercompellingusecaseis

interactive

datamining,whereauserrunsmultiplead-

hocqueriesonthesamesubsetofthedata.Unfortu-

nately,inmostcurrentframeworks,theonlywaytoreuse

databetweencomputations(

e.g.,

betweentwoMapRe-

ducejobs)istowriteittoanexternalstablestoragesys-

tem,

e.g.,

adistributedsystem.Thisincurssubstantial

overheadsduetodatareplication,diskI/O,andserializa-

tion,whichcandominateapplicationexecutiontimes.

Recognizingthisproblem,researchershavedeveloped

specializedframeworksforsomeapplicationsthatre-

quiredatareuse.Forexample,Pregel[22]isasystemfor

iterativegraphcomputationsthatkeepsintermediatedata

inmemory,whileHaLoop[7]offersaniterativeMapRe-

duceinterface.However,theseframeworksonlysupport

computationpatterns(

e.g.,

loopingaseriesof

MapReducesteps),andperformdatasharingimplicitly

forthesepatterns.Theydonotprovideabstractionsfor

moregeneralreuse,

e.g.,

toletauserloadseveraldatasets

intomemoryandrunad-hocqueriesacrossthem.

Inthispaper,weproposeanewabstractioncalled

re-

silientdistributeddatasets(RDDs)

thatenablesef

datareuseinabroadrangeofapplications.RDDsare

fault-tolerant,paralleldatastructuresthatletusersex-

plicitlypersistintermediateresultsinmemory,control

theirpartitioningtooptimizedataplacement,andma-

nipulatethemusingarichsetofoperators.

ThemainchallengeindesigningRDDsisa

programminginterfacethatcanprovidefaulttolerance

ef

.Existingabstractionsforin-memorystorage

onclusters,suchasdistributedsharedmemory[24],key-

valuestores[25],databases,andPiccolo[27],offeran

interfacebasedonupdatestomutablestate

(

e.g.,

cellsinatable).Withthisinterface,theonlyways

toprovidefaulttolerancearetoreplicatethedataacross

machinesortologupdatesacrossmachines.Bothap-

proachesareexpensivefordata-intensiveworkloads,as

theyrequirecopyinglargeamountsofdataovertheclus-

ternetwork,whosebandwidthisfarlowerthanthatof

RAM,andtheyincursubstantialstorageoverhead.

Incontrasttothesesystems,RDDsprovideaninter-

facebasedon

coarse-grained

transformations(

e.g.,

map,

andjoin)thatapplythesameoperationtomany

dataitems.Thisallowsthemtoefprovidefault

tolerancebyloggingthetransformationsusedtobuilda

dataset(its

lineage

)ratherthantheactualdata.

1

Ifaparti-

tionofanRDDislost,theRDDhasenoughinformation

abouthowitwasderivedfromotherRDDstorecompute

1

CheckpointingthedatainsomeRDDsmaybeusefulwhenalin-

eagechaingrowslarge,however,andwediscusshowtodoitin

x

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Amended

Safety Assessment of

Methylisothiazolinone

as Used in Cosmetics

Status:

Draft

Amended

Report

for Panel Review

Release Date:

February

21, 2014

Panel Meeting Date:

March 17

-18, 2014

The 201

4 Cosmetic Ingredient Review Expert Panel members are: Chairman, Wilma F. Bergfeld, M.D., F.A.C.P.;

Donald V. Belsito, M.D.; Curtis D. Klaassen, Ph.D.; Daniel C. Liebler, Ph.D.; Ronald A. Hill, Ph.D. James G.

Marks, Jr., M.D.; Ronald C. Shank, Ph.D.; Thomas

J. Slaga, Ph.D.; and Paul W. Snyder, D.V.M., Ph.D. The CIR

Director

is Lillian J. Gill

, DPA. This safety assessment was prepared by Christina L. Burnett, Scientific

Analyst/Writer

.

© Cosmetic Ingredient Review

1620 L St NW, Suite 1200

Washington,

DC 20036

-4702 ph 202.331.0651

fax 202.331.0088

cirinfo@cir

-safety.org

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adegenet.r-forge.r-project.org

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AtutorialforDiscriminantAnalysisofPrincipal

Components(DAPC)using

adegenet

2.0.0

ThibautJombart,CaitlinCollins

ImperialCollegeLondon

MRCCentreforOutbreakAnalysisandModelling

June23,2015

Abstract

ThisvignetteprovidesatutorialforapplyingtheDiscriminantAnalysisofPrincipal

Components(DAPC[

1

])usingthe

adegenet

package[

2

]fortheRsoftware[

3

].This

methodsaimstoidentifyanddescribegeneticclusters,althoughitcaninfactbeapplied

toanyquantitativedata.Weillustratehowtouse

find.clusters

toidentifyclusters,

and

dapc

todescribetherelationshipsbetweentheseclusters.Moreadvancedtopics

arethenintroduced,suchasadvancedgraphics,assessingthestabilityofDAPCresults

andusingsupplementaryindividuals.

tjombart@imperial.ac.uk

,

caitlin.collins12@imperial.ac.uk

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1Extending the Table

Strategic Plan 2015-2020

From an Agrarian Jesus

to an Urban James

GREBEL NOW

Conrad Grebel University College Magazine

New in Town – page 10 | Convocation 2015 – page 16

Re˜ections on a Sandal – page 19 | Dean™s Pick – page 20 | New Faculty – page 26

Vol. 31 No. 2

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