decentralized-id.github.io/collections/_2018-W3C-Authentication-Identity-Workshop/13.5-Day-2-Attestation-Arm.html

---
title: "13) Day 2 Attestation Arm"
---
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class="t m0 x1 h1 y1 ff1 fs0 fc0 sc0 ls0 ws0">Att<span class="_ _0"></span>es<span class="_ _1"></span>t<span class="_ _0"></span>at<span class="_ _2"></span>i<span class="_ _0"></span>o<span class="_ _1"></span>n <span class="_ _3"></span>Roadmap<span class="_ _1"></span><span class="ff2"> </span></div><div class="t m0 x2 h2 y2 ff2 fs1 fc1 sc0 ls0 ws0">Ma<span class="_ _1"></span>t<span class="_ _0"></span>h<span class="_ _1"></span>ia<span class="_ _1"></span>s <span class="_ _4"></span>B<span class="_ _2"></span>ros<span class="_ _1"></span>sard<span class="_ _1"></span> </div><div class="t m0 x3 h2 y3 ff2 fs1 fc2 sc0 ls0 ws0">m<span class="_ _2"></span>athi<span class="_ _1"></span>as<span class="_ _1"></span>.bro<span class="_ _1"></span>ss<span class="_ _0"></span>a<span class="_ _1"></span>rd<span class="_ _0"></span>@<span class="_ _0"></span>a<span class="_ _1"></span>rm<span class="_ _0"></span>.<span class="_ _1"></span>c<span class="_ _2"></span>o<span class="_ _0"></span>m<span class="fc1"> </span></div><div class="t m0 x4 h2 y4 ff2 fs1 fc1 sc0 ls0 ws0"> </div><div class="t m0 x5 h3 y5 ff2 fs2 fc1 sc0 ls0 ws0">W3C <span class="_ _0"></span>Work<span class="_ _1"></span>sh<span class="_ _1"></span>op<span class="_ _1"></span> <span class="_ _4"></span>on<span class="_ _1"></span> <span class="_ _5"></span>Stron<span class="_ _1"></span>g </div><div class="t m0 x6 h2 y6 ff2 fs1 fc1 sc0 ls0 ws0">Au<span class="_ _1"></span>t<span class="_ _0"></span>h<span class="_ _1"></span>e<span class="_ _0"></span>n<span class="_ _1"></span>t<span class="_ _2"></span>icatio<span class="_ _1"></span>n <span class="_ _4"></span>&amp; Id<span class="_ _1"></span>e<span class="_ _0"></span>n<span class="_ _1"></span>t<span class="_ _0"></span>i<span class="_ _1"></span>t<span class="_ _0"></span>y </div><div class="t m0 x7 h2 y7 ff2 fs1 fc1 sc0 ls0 ws0">D<span class="_ _0"></span>ece<span class="_ _0"></span>m<span class="_ _2"></span>b<span class="_ _1"></span>e<span class="_ _0"></span>r 1<span class="_ _1"></span>0t<span class="_ _2"></span>h<span class="_ _1"></span> <span class="_ _0"></span>an<span class="_ _1"></span>d <span class="_ _3"></span>11th,<span class="_ _1"></span> <span class="_ _5"></span>201<span class="_ _1"></span>8 </div><div class="t m0 x4 h2 y8 ff2 fs1 fc1 sc0 ls0 ws0"> </div><div class="t m0 x4 h2 y9 ff2 fs1 fc1 sc0 ls0 ws0"> </div><a class="l" href="mailto:mathias.brossard@arm.com"><div class="d m1" style="border-style:none;position:absolute;left:173.930000px;bottom:278.800000px;width:372.370000px;height:39.800000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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class="t m0 x8 h4 ya ff2 fs3 fc0 sc0 ls0 ws0">P<span class="_ _2"></span>la<span class="_ _0"></span>tform<span class="_ _0"></span> <span class="_ _6"></span>Se<span class="_ _0"></span>c<span class="_ _2"></span>ur<span class="_ _2"></span>ity <span class="_ _1"></span>A<span class="_ _1"></span>r<span class="_ _0"></span>chi<span class="_ _2"></span>tec<span class="_ _0"></span>tu<span class="_ _1"></span>r<span class="_ _0"></span>e<span class="_ _2"></span> (P<span class="_ _0"></span>SA)<span class="_ _1"></span> </div><div class="t m0 x9 h5 yb ff3 fs4 fc0 sc0 ls0 ws0">–<span class="_ _7"> </span><span class="ff2">Io<span class="_ _2"></span>T <span class="_ _1"></span>se<span class="_ _2"></span>cur<span class="_ _2"></span>ity<span class="_ _1"></span> <span class="_ _6"></span>de<span class="_ _2"></span>spe<span class="_ _0"></span>r<span class="_ _2"></span>ate<span class="_ _0"></span>ly<span class="_ _1"></span> </span></div><div class="t m0 xa h5 yc ff2 fs4 fc0 sc0 ls0 ws0">ne<span class="_ _0"></span>ede<span class="_ _0"></span>d.<span class="_ _2"></span> <span class="_ _8"> </span> </div><div class="t m0 x9 h5 yd ff3 fs4 fc0 sc0 ls0 ws0">–<span class="_ _7"> </span><span class="ff2">PS<span class="_ _1"></span>A<span class="_ _2"></span> aim<span class="_ _1"></span>s<span class="_ _0"></span> <span class="_ _1"></span>to of<span class="_ _0"></span>fe<span class="_ _0"></span>r <span class="_ _8"> </span>guidance<span class="_ _0"></span> <span class="_ _6"></span>+ </span></div><div class="t m0 xa h5 ye ff2 fs4 fc0 sc0 ls0 ws0">de<span class="_ _0"></span>signs <span class="_ _6"></span>+<span class="_ _2"></span> <span class="_ _6"></span>sof<span class="_ _0"></span>tware<span class="_ _2"></span> </div><div class="t m0 x9 h5 yf ff3 fs4 fc0 sc0 ls0 ws0">–<span class="_ _7"> </span><span class="ff2">Ope<span class="_ _0"></span>n <span class="_ _9"></span>Source<span class="_ _0"></span> <span class="_ _9"></span>pr<span class="_ _2"></span>oje<span class="_ _0"></span>ct:<span class="_ _1"></span> <span class="_ _a"></span>Ar<span class="_ _0"></span>m<span class="_ _1"></span> </span></div><div class="t m0 xa h6 y10 ff2 fs5 fc0 sc0 ls0 ws0">Tr<span class="_ _0"></span>uste<span class="_ _0"></span>d <span class="_ _b"> </span>F<span class="_ _1"></span>ir<span class="_ _0"></span>m<span class="_ _1"></span>war<span class="_ _0"></span>e-<span class="_ _2"></span>M </div><div class="t m0 x9 h5 y11 ff3 fs4 fc0 sc0 ls0 ws0">–<span class="_ _7"> </span><span class="ff2">Ar<span class="_ _0"></span>m<span class="_ _1"></span> <span class="_ _6"></span>v<span class="_ _0"></span>8<span class="_ _1"></span>-<span class="_ _2"></span>M <span class="_ _6"></span>with <span class="_ _1"></span>T<span class="_ _0"></span>rus<span class="_ _2"></span>tZone<span class="_ _0"></span> </span></div><div class="t m0 xa h5 y12 ff2 fs4 fc0 sc0 ls0 ws0">suppo<span class="_ _0"></span>rt </div><div class="t m0 x9 h5 y13 ff3 fs4 fc0 sc0 ls0 ws0">–<span class="_ _7"> </span><span class="ff2">API<span class="_ _2"></span>s <span class="_ _6"></span>cur<span class="_ _2"></span>re<span class="_ _0"></span>ntly<span class="_ _1"></span> <span class="_ _6"></span>be<span class="_ _0"></span>i<span class="_ _1"></span>ng <span class="_ _6"></span>de<span class="_ _0"></span>fine<span class="_ _0"></span>d<span class="_ _1"></span> </span></div><div class="t m0 xb h7 y14 ff3 fs6 fc0 sc0 ls0 ws0">•</div><div class="t m0 xc h8 y15 ff2 fs6 fc2 sc0 ls0 ws0">C<span class="_ _1"></span>r<span class="_ _0"></span>yp<span class="_ _0"></span>to<span class="_ _0"></span> <span class="_ _6"></span>API p<span class="_ _2"></span>u<span class="_ _2"></span>b<span class="_ _0"></span>l<span class="_ _1"></span>is<span class="_ _1"></span>hed<span class="fc0"> </span></div><div class="t m0 xb h8 y16 ff3 fs6 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff2">At<span class="_ _0"></span>tes<span class="_ _1"></span>t<span class="_ _0"></span>a<span class="_ _1"></span>t<span class="_ _2"></span>ion<span class="_ _0"></span> <span class="_ _9"></span>AP<span class="_ _0"></span>I <span class="_ _1"></span>p<span class="_ _0"></span>l<span class="_ _1"></span>ann<span class="_ _0"></span>ed for<span class="_ _0"></span> <span class="_ _6"></span>1Q </span></div><div class="t m0 xc h8 y17 ff2 fs6 fc0 sc0 ls1 ws0">2019<span class="ls0"> </span></div><div class="t m0 xd h5 y18 ff2 fs4 fc0 sc0 ls0 ws0"> </div><div class="t m0 xd h5 y19 ff2 fs4 fc0 sc0 ls0 ws0"> </div><div class="t m0 xd h5 y1a ff2 fs4 fc0 sc0 ls0 ws0"> </div><div class="t m0 xe h9 y1b ff2 fs7 fc3 sc0 ls0 ws0">SECU<span class="_ _1"></span>RE <span class="_ _5"></span>STA<span class="_ _1"></span>TE<span class="_ _1"></span>S<span class="ff4 fc0"> <span class="_ _c"></span><span class="ff2 fc3">NON-S<span class="_ _2"></span>EC<span class="_ _1"></span>URE <span class="_ _3"></span>STA<span class="_ _1"></span>TE<span class="_ _0"></span>S<span class="ff4 fc0"> </span></span></span></div><div class="t m0 xf ha y1c ff2 fs8 fc3 sc0 ls0 ws0">Tru<span class="_ _1"></span>s<span class="_ _0"></span>tZon<span class="_ _1"></span>e <span class="_ _2"></span>f<span class="_ _2"></span>o<span class="_ _1"></span>r<span class="_ _0"></span> <span class="_ _1"></span>Arm<span class="_ _2"></span> <span class="_ _1"></span>v8-<span class="_ _0"></span>M<span class="_ _1"></span><span class="ff4 fc0"> </span></div><div class="t m0 x10 ha y1d ff2 fs8 fc3 sc0 ls0 ws0">Secure </div><div class="t m0 x11 ha y1e ff2 fs8 fc3 sc0 ls0 ws0">Ap<span class="_ _1"></span>p/Lib<span class="_ _1"></span>s</div><div class="t m0 x12 hb y1f ff4 fs8 fc0 sc0 ls0 ws0"> </div><div class="t m0 x13 ha y20 ff2 fs8 fc3 sc0 ls0 ws0">Secu<span class="_ _1"></span>r<span class="_ _0"></span>e <span class="_ _1"></span>OS<span class="ff4 fc0"> </span></div><div class="t m0 x14 ha y21 ff2 fs8 fc3 sc0 ls0 ws0">p<span class="_ _1"></span>arts<span class="_ _0"></span><span class="ff4 fc0"> </span></div><div class="t m0 x15 ha y22 ff2 fs8 fc3 sc0 ls0 ws0">N<span class="_ _1"></span>on<span class="_ _1"></span>-<span class="_ _0"></span>secure </div><div class="t m0 x16 ha y23 ff2 fs8 fc3 sc0 ls0 ws0">OS <span class="_ _2"></span>p<span class="_ _1"></span>arts</div><div class="t m0 x17 hb y24 ff4 fs8 fc0 sc0 ls0 ws0"> </div><div class="t m0 x18 ha y25 ff2 fs8 fc3 sc0 ls0 ws0">N<span class="_ _1"></span>on<span class="_ _1"></span>-<span class="_ _2"></span>s<span class="_ _0"></span>ecu<span class="_ _1"></span>r<span class="_ _0"></span>e<span class="_ _1"></span> </div><div class="t m0 x19 ha y26 ff2 fs8 fc3 sc0 ls0 ws0">Ap<span class="_ _1"></span>p<span class="ff4 fc0"> </span></div><div class="t m0 x1a hc y27 ff2 fs9 fc0 sc0 ls0 ws0">AP<span class="_ _2"></span>Is<span class="_ _2"></span> </div><a class="l" href="https://github.com/ARMmbed/mbed-crypto"><div class="d m1" style="border-style:none;position:absolute;left:133.330000px;bottom:208.950000px;width:101.370000px;height:14.280000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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class="t m0 x1b h1 y28 ff2 fs0 fc0 sc0 ls0 ws0">Atte<span class="_ _2"></span>stati<span class="_ _1"></span>on </div><div class="t m0 x9 hd y29 ff3 fsa fc0 sc0 ls0 ws0">–<span class="_ _d"> </span><span class="ff2">M<span class="_ _2"></span>a<span class="_ _2"></span>ny<span class="_ _2"></span> <span class="_ _1"></span>exis<span class="_ _1"></span>ti<span class="_ _2"></span>ng a<span class="_ _0"></span>t<span class="_ _1"></span>tes<span class="_ _1"></span>tati<span class="_ _2"></span>on <span class="_ _3"></span>ef<span class="_ _1"></span>forts<span class="_ _1"></span>:<span class="_ _9"></span> </span></div><div class="t m0 xb he y2a ff3 fsb fc0 sc0 ls0 ws0">•<span class="_ _e"> </span><span class="ff2">T<span class="_ _1"></span>r<span class="_ _0"></span>u<span class="_ _1"></span>st<span class="_ _1"></span>ed <span class="_ _3"></span>Com<span class="_ _1"></span>puting <span class="_ _3"></span>Gr<span class="_ _0"></span>ou<span class="_ _1"></span>p (TCG):<span class="_ _1"></span> <span class="_ _3"></span>T<span class="_ _1"></span>r<span class="_ _0"></span>u<span class="_ _1"></span>st<span class="_ _1"></span>ed <span class="_ _4"></span>P<span class="_ _1"></span>la<span class="_ _0"></span>tf<span class="_ _1"></span>orm </span></div><div class="t m0 xc hd y2b ff2 fsa fc0 sc0 ls0 ws0">M<span class="_ _2"></span>odu<span class="_ _1"></span>l<span class="_ _0"></span>e (T<span class="_ _1"></span>P<span class="_ _1"></span>M<span class="_ _0"></span>)<span class="_ _1"></span> </div><div class="t m0 xb hd y2c ff3 fsa fc0 sc0 ls0 ws0">•<span class="_ _e"> </span><span class="ff2">A<span class="_ _1"></span>ndroid </span></div><div class="t m0 xb he y2d ff3 fsb fc0 sc0 ls0 ws0">•<span class="_ _e"> </span><span class="ff2">FID<span class="_ _1"></span>O </span></div><div class="t m0 xb hd y2e ff3 fsa fc0 sc0 ls0 ws0">•<span class="_ _e"> </span><span class="ff2">Gl<span class="_ _0"></span>ob<span class="_ _1"></span>a<span class="_ _0"></span>l <span class="_ _1"></span>P<span class="_ _1"></span>l<span class="_ _0"></span>atfo<span class="_ _1"></span>r<span class="_ _0"></span>m<span class="_ _6"></span> </span></div><div class="t m0 xb hd y2f ff3 fsa fc0 sc0 ls0 ws0">•<span class="_ _e"> </span><span class="ff5">…<span class="ff2"> </span></span></div><div class="t m0 x9 he y30 ff3 fsb fc0 sc0 ls0 ws0">–<span class="_ _d"> </span><span class="ff2">Ex<span class="_ _1"></span>press<span class="_ _1"></span>ed <span class="_ _f"></span>i<span class="_ _2"></span>nt<span class="_ _1"></span>er<span class="_ _0"></span>es<span class="_ _1"></span>t<span class="_ _1"></span> <span class="_ _0"></span>f<span class="_ _1"></span>or<span class="_ _2"></span> som<span class="_ _1"></span>e <span class="_ _3"></span>c<span class="_ _1"></span>onv<span class="_ _0"></span>ergenc<span class="_ _1"></span>e <span class="_ _3"></span>towards<span class="_ _1"></span> <span class="_ _5"></span>IE<span class="_ _1"></span>TF<span class="_ _1"></span> </span></div><div class="t m0 xa hd y31 ff2 fsa fc0 sc0 ls0 ws0">EA<span class="_ _1"></span>T<span class="_ _1"></span> </div><div class="t m0 xb he y32 ff3 fsb fc0 sc0 ls0 ws0">•<span class="_ _e"> </span><span class="ff2">CW<span class="_ _0"></span>T<span class="_ _1"></span> <span class="_ _2"></span>/<span class="_ _2"></span> <span class="_ _1"></span>JW<span class="_ _0"></span>T<span class="_ _1"></span> <span class="_ _0"></span>b<span class="_ _1"></span>a<span class="_ _0"></span>s<span class="_ _1"></span>ed con<span class="_ _1"></span>ta<span class="_ _2"></span>i<span class="_ _2"></span>ner<span class="_ _1"></span> </span></div><div class="t m0 xb hd y33 ff3 fsa fc0 sc0 ls0 ws0">•<span class="_ _e"> </span><span class="ff2">CW<span class="_ _0"></span>T<span class="_ _1"></span> preferr<span class="_ _2"></span>ed i<span class="_ _2"></span>n <span class="_ _1"></span>c<span class="_ _1"></span>ons<span class="_ _1"></span>trai<span class="_ _0"></span>ned<span class="_ _1"></span> <span class="_ _3"></span>env<span class="_ _2"></span>ir<span class="_ _0"></span>o<span class="_ _1"></span>nmen<span class="_ _1"></span>ts<span class="_ _1"></span>  </span></div><div class="t m0 xc hd y34 ff2 fsa fc0 sc0 ls0 ws0">(t<span class="_ _1"></span>oken<span class="_ _1"></span> <span class="_ _3"></span>s<span class="_ _1"></span>i<span class="_ _0"></span>z<span class="_ _1"></span>e, <span class="_ _3"></span>R<span class="_ _0"></span>A<span class="_ _1"></span>M/<span class="_ _0"></span>Cod<span class="_ _1"></span>e <span class="_ _1"></span>r<span class="_ _2"></span>equiremen<span class="_ _1"></span>ts<span class="_ _1"></span>)<span class="_ _6"></span> </div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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class="t m0 x1c h1 y28 ff2 fs0 fc0 sc0 ls0 ws0">Roa<span class="_ _0"></span>dmap </div><div class="t m0 x9 hf y35 ff3 fsc fc0 sc0 ls0 ws0">–<span class="_ _10"> </span><span class="ff2">EA<span class="_ _0"></span>T <span class="_ _1"></span>i<span class="_ _2"></span>s<span class="_ _0"></span> <span class="_ _a"></span>t<span class="_ _0"></span>he<span class="_ _0"></span> <span class="_ _9"></span>s<span class="_ _2"></span>t<span class="_ _2"></span>arti<span class="_ _0"></span>n<span class="_ _2"></span>g<span class="_ _0"></span> <span class="_ _11"></span>p<span class="_ _0"></span>o<span class="_ _1"></span>i<span class="_ _2"></span>n<span class="_ _0"></span>t </span></div><div class="t m0 x9 h10 y36 ff3 fsd fc0 sc0 ls0 ws0">–<span class="_ _10"> </span><span class="ff2">In<span class="_ _0"></span>t<span class="_ _2"></span>e<span class="_ _0"></span>ro<span class="_ _1"></span>pe<span class="_ _0"></span>rabi<span class="_ _0"></span>li<span class="_ _0"></span>t<span class="_ _0"></span>y<span class="_ _1"></span> <span class="_ _12"> </span>Mo<span class="_ _1"></span>de<span class="_ _0"></span>l </span></div><div class="t m0 x9 h10 y37 ff3 fsd fc0 sc0 ls0 ws0">–<span class="_ _10"> </span><span class="ff2">Tru<span class="_ _2"></span>s<span class="_ _0"></span>t <span class="_ _9"></span>Mo<span class="_ _1"></span>d<span class="_ _2"></span>e<span class="_ _0"></span>l(<span class="_ _2"></span>s<span class="_ _0"></span>)<span class="_ _1"></span> </span></div><div class="t m0 x9 h10 y38 ff3 fsd fc0 sc0 ls0 ws0">–<span class="_ _10"> </span><span class="ff2">Pro<span class="_ _1"></span>t<span class="_ _0"></span>o<span class="_ _1"></span>co<span class="_ _1"></span>l<span class="_ _0"></span>s </span></div><div class="t m0 xb h10 y39 ff3 fsd fc0 sc0 ls0 ws0">•<span class="_ _13"> </span><span class="ff2">Re<span class="_ _0"></span>m<span class="_ _1"></span>o<span class="_ _1"></span>t<span class="_ _0"></span>e <span class="_ _6"></span>At<span class="_ _0"></span>te<span class="_ _0"></span>s<span class="_ _2"></span>t<span class="_ _0"></span>a<span class="_ _1"></span>t<span class="_ _0"></span>ion </span></div><div class="t m0 xb h10 y3a ff3 fsd fc0 sc0 ls0 ws0">•<span class="_ _13"> </span><span class="ff2">I<span class="_ _2"></span>n<span class="_ _2"></span>t<span class="_ _0"></span>eg<span class="_ _0"></span>ra<span class="_ _1"></span>t<span class="_ _0"></span>ion <span class="_ _8"></span>w<span class="_ _0"></span>it<span class="_ _0"></span>h <span class="_ _a"></span>Enroll<span class="_ _0"></span>m<span class="_ _1"></span>e<span class="_ _0"></span>n<span class="_ _2"></span>t<span class="_ _2"></span> <span class="_ _b"></span>/ <span class="_ _1"></span>Cre<span class="_ _2"></span>d<span class="_ _0"></span>en<span class="_ _0"></span>ti<span class="_ _0"></span>ali<span class="_ _0"></span>n<span class="_ _1"></span>g<span class="_ _0"></span> <span class="_ _14"> </span>/ </span></div><div class="t m0 xc h10 y3b ff2 fsd fc0 sc0 ls2 ws0">On<span class="_ _0"></span><span class="ls0">-<span class="_ _0"></span>boa<span class="_ _1"></span>rd<span class="_ _0"></span>in<span class="_ _0"></span>g </span></div><div class="t m0 xb hf y3c ff3 fsc fc0 sc0 ls0 ws0">•<span class="_ _13"> </span><span class="ff2">I<span class="_ _2"></span>n<span class="_ _0"></span>te<span class="_ _0"></span>grat<span class="_ _2"></span>i<span class="_ _0"></span>o<span class="_ _1"></span>n<span class="_ _2"></span> <span class="_ _8"></span>wi<span class="_ _0"></span>t<span class="_ _2"></span>h<span class="_ _0"></span> <span class="_ _15"></span>A<span class="_ _0"></span>ut<span class="_ _0"></span>he<span class="_ _0"></span>nt<span class="_ _0"></span>i<span class="_ _0"></span>c<span class="_ _1"></span>ati<span class="_ _0"></span>o<span class="_ _9"></span>n </span></div><div class="t m0 xb hf y3d ff3 fsc fc0 sc0 ls0 ws0">•<span class="_ _13"> </span><span class="ff2">I<span class="_ _2"></span>n<span class="_ _0"></span>te<span class="_ _0"></span>grat<span class="_ _2"></span>i<span class="_ _0"></span>o<span class="_ _1"></span>n<span class="_ _2"></span> <span class="_ _8"></span>wi<span class="_ _0"></span>t<span class="_ _2"></span>h<span class="_ _0"></span> <span class="_ _15"></span>A<span class="_ _0"></span>ut<span class="_ _0"></span>horiza<span class="_ _1"></span>t<span class="_ _0"></span>i<span class="_ _2"></span>o<span class="_ _1"></span>n </span></div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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class="t m0 x1d h1 y28 ff2 fs0 fc0 sc0 ls0 ws0">P<span class="_ _0"></span>art<span class="_ _1"></span>ing <span class="_ _3"></span>t<span class="_ _1"></span>houghts<span class="_ _1"></span> </div><div class="t m0 x9 hf y3e ff3 fsc fc0 sc0 ls0 ws0">–<span class="_ _10"> </span><span class="ff2">In<span class="_ _0"></span>creas<span class="_ _2"></span>i<span class="_ _0"></span>ng<span class="_ _0"></span>l<span class="_ _2"></span>y <span class="_ _11"></span>d<span class="_ _0"></span>is<span class="_ _0"></span>agg<span class="_ _0"></span>reg<span class="_ _0"></span>ate<span class="_ _0"></span>d <span class="_ _12"> </span>s<span class="_ _0"></span>y<span class="_ _1"></span>s<span class="_ _0"></span>t<span class="_ _2"></span>e<span class="_ _0"></span>m<span class="_ _1"></span>s<span class="_ _0"></span> <span class="_ _15"></span>(Cl<span class="_ _0"></span>o<span class="_ _1"></span>u<span class="_ _2"></span>d<span class="_ _0"></span>, <span class="_ _b"></span>IoT, </span></div><div class="t m0 xa h10 y3f ff2 fsd fc0 sc0 ls0 ws0">We<span class="_ _2"></span>b<span class="_ _0"></span>) <span class="_ _9"></span>wi<span class="_ _0"></span>t<span class="_ _2"></span>h<span class="_ _0"></span> <span class="_ _15"></span>m<span class="_ _1"></span>an<span class="_ _0"></span>y<span class="_ _1"></span> <span class="_ _1"></span>co<span class="_ _1"></span>mpone<span class="_ _0"></span>nt<span class="_ _0"></span>s<span class="_ _2"></span>. </div><div class="t m0 x9 h10 y40 ff3 fsd fc0 sc0 ls0 ws0">–<span class="_ _10"> </span><span class="ff2">A<span class="_ _2"></span>t<span class="_ _0"></span>te<span class="_ _0"></span>st<span class="_ _0"></span>ati<span class="_ _0"></span>o<span class="_ _1"></span>n<span class="_ _0"></span> <span class="_ _14"> </span>i<span class="_ _2"></span>s<span class="_ _0"></span> <span class="_ _9"></span>a<span class="_ _1"></span> <span class="_ _0"></span>bu<span class="_ _0"></span>il<span class="_ _0"></span>d<span class="_ _0"></span>in<span class="_ _0"></span>g <span class="_ _16"> </span>b<span class="_ _0"></span>loc<span class="_ _1"></span>k <span class="_ _1"></span>for <span class="_ _1"></span>tru<span class="_ _0"></span>s<span class="_ _2"></span>t<span class="_ _2"></span> <span class="_ _b"></span>i<span class="_ _2"></span>n<span class="_ _0"></span> </span></div><div class="t m0 xa h10 y41 ff2 fsd fc0 sc0 ls0 ws0">co<span class="_ _1"></span>n<span class="_ _2"></span>n<span class="_ _0"></span>ecte<span class="_ _0"></span>d<span class="_ _2"></span> <span class="_ _b"></span>s<span class="_ _2"></span>yst<span class="_ _0"></span>e<span class="_ _2"></span>m<span class="_ _1"></span>s<span class="_ _0"></span>. </div><div class="t m0 x1e h10 y42 ff2 fsd fc0 sc0 ls0 ws0"> </div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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