From a7e0d88e27ffe43214750f07f24993b9f352a53c Mon Sep 17 00:00:00 2001 From: Djura Smits Date: Mon, 29 Jul 2024 16:37:15 +0200 Subject: [PATCH 1/8] Added some pictures --- episodes/chap1_introduction_FL.md | 6 ++++++ episodes/fig/federated learning.jpg | Bin 0 -> 47993 bytes episodes/fig/federated sum.jpg | Bin 0 -> 46014 bytes episodes/fig/mpc.jpg | Bin 0 -> 55069 bytes 4 files changed, 6 insertions(+) create mode 100644 episodes/fig/federated learning.jpg create mode 100644 episodes/fig/federated sum.jpg create mode 100644 episodes/fig/mpc.jpg diff --git a/episodes/chap1_introduction_FL.md b/episodes/chap1_introduction_FL.md index 9c44c1b6..3bde6169 100644 --- a/episodes/chap1_introduction_FL.md +++ b/episodes/chap1_introduction_FL.md @@ -98,6 +98,8 @@ statistics like the mean, or it can be more intricate like a machine learning mo Aggregating the data does not ensure complete protection of person-level information, but it certainly makes it less likely that this will happen. +![General flow of a federated analysis](fig/federated_sum.jpg) + TODO: Example of data leakage in simple aggregated case ## Federated learning @@ -109,6 +111,8 @@ traditional federated learning, the clients train machine learning models, and o the models are sent back to the central server. The central server combines the updates from all the individual clients into one final machine learning model. +![In federated learning only the gradients of models are shared](fig/federated%20learning.jpg) + There are caveats to using this type of data analysis though. Although the data transmitted from the clients to the server are an aggregation of the raw data, researchers have found a way to use this data to reconstruct the original data. This vulnerability is called _gradient leakage_. @@ -133,6 +137,8 @@ retrieved. When combining multiple values using secret sharing, this will result in the parties owning new puzzle pieces that when put together will reveal the result of the computation. +![In secure multiparty computation parties collaboratively perform an analysis while holding only encrypted pieces of the data](fig/mpc.jpg) + ::: callout ### Secret sharing, an example diff --git a/episodes/fig/federated learning.jpg b/episodes/fig/federated learning.jpg new file mode 100644 index 0000000000000000000000000000000000000000..67a75b35e5995cd74c5a26dde23f8bd686cdbe98 GIT binary patch literal 47993 zcmeFZcU+U*wk{e)ih`i@9u))x5s{{}h)NSNASLuDsDL!-5kjIORf>R!f)bPt0@9^O zq=P6(je?Yf-V;g)q}|uG*0=UqWuJZb`Rm+$zsMy1AalNRlsV@!o-roOAIvGxk*fyA z2B3ZW_JM8!|3J(!&}9(I{{6qdfY$-wihxz~8FXm^^QI7rJ zSwSrOPJs3w-N$ltAF~Yv27&gm0<--+;lI81?FZ(_#?Eo@5GU|~>LZ~2`&d}^A7J@C zYv9vi!0$l^jt?ffan(QV&y=8wm)_7rw{z;1AqF!pFZ%X5B%u^fBL|mKJcdx{OJRK`oNz) z@TU*_=>z{AA1F)LJ&SJEYvQE|P;l>%>bPoauS=^n_v|8?L~#apS-uDu0M&`zHNq)H@lKP%7 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chapter1}/federated learning.jpg (100%) rename episodes/fig/{ => chapter1}/federated sum.jpg (100%) rename episodes/fig/{ => chapter1}/gradient_leakage.jpg (100%) create mode 100644 episodes/fig/chapter1/horizontal_vertical_partitioning.jpg rename episodes/fig/{ => chapter1}/mpc.jpg (100%) diff --git a/episodes/chap1_introduction_FL.md b/episodes/chap1_introduction_FL.md index 3bde6169..6d7d5703 100644 --- a/episodes/chap1_introduction_FL.md +++ b/episodes/chap1_introduction_FL.md @@ -38,7 +38,7 @@ In classic data analysis, all data is copied over into a single place. This make use conventional data analysis software and tools to gain insights. -![In classic analysis all the data is brought together](fig/classic%20analysis.jpg){alt='Two tables +![In classic analysis all the data is brought together](fig/chapter1/classic%20analysis.jpg){alt='Two tables with data are moved to a central location'} Unfortunately this way of working does not respect the privacy of the people contained within the @@ -98,7 +98,7 @@ statistics like the mean, or it can be more intricate like a machine learning mo Aggregating the data does not ensure complete protection of person-level information, but it certainly makes it less likely that this will happen. -![General flow of a federated analysis](fig/federated_sum.jpg) +![General flow of a federated analysis](fig/chapter1/federated%20sum.jpg) TODO: Example of data leakage in simple aggregated case @@ -111,13 +111,13 @@ traditional federated learning, the clients train machine learning models, and o the models are sent back to the central server. The central server combines the updates from all the individual clients into one final machine learning model. -![In federated learning only the gradients of models are shared](fig/federated%20learning.jpg) +![In federated learning only the gradients of models are shared](fig/chapter1/federated%20learning.jpg) There are caveats to using this type of data analysis though. Although the data transmitted from the clients to the server are an aggregation of the raw data, researchers have found a way to use this data to reconstruct the original data. This vulnerability is called _gradient leakage_. -![An example of gradient leakage](fig/gradient_leakage.jpg) +![An example of gradient leakage](fig/chapter1/gradient_leakage.jpg) ## Secure Multiparty Computation @@ -137,7 +137,7 @@ retrieved. When combining multiple values using secret sharing, this will result in the parties owning new puzzle pieces that when put together will reveal the result of the computation. -![In secure multiparty computation parties collaboratively perform an analysis while holding only encrypted pieces of the data](fig/mpc.jpg) +![In secure multiparty computation parties collaboratively perform an analysis while holding only encrypted pieces of the data](fig/chapter1/mpc.jpg) ::: callout @@ -179,6 +179,9 @@ selection of inputs with random noise. A single individual will then always be a their data has contributed to the final result. An individual has _plausible deniability_ with regards to whether it was part of the dataset. +![Differential privacy replaces a subset of the data with random values](fig/chapter1/differential%20privacy.jpg) + + ## Blocks upon blocks The previously mentioned techniques are not used in isolation, but are usually stacked on top of @@ -210,6 +213,8 @@ of privacy enhancing algorithms than horizontal partitioning. Data can even be horizontally and vertically partitioned at the same time. In these cases, it may be necessary to combine multiple techniques. +![Horizontal and vertical partitioning refers to how data is separated](fig/chapter1/horizontal_vertical_partitioning.jpg) + ## Technology doesn't solve everything You have now learned about various technologies for analyzing data while preserving privacy of diff --git a/episodes/fig/data_anonymization.jpg b/episodes/fig/chapter1/data_anonymization.jpg similarity index 100% rename from episodes/fig/data_anonymization.jpg rename to episodes/fig/chapter1/data_anonymization.jpg diff --git a/episodes/fig/chapter1/differential privacy.jpg b/episodes/fig/chapter1/differential privacy.jpg new file mode 100644 index 0000000000000000000000000000000000000000..c125ab5a9be386e3e55177ea282048825a7229eb GIT binary patch literal 45272 zcmeFZ2UOG1mNy!tNtY%~h)PvNlp<0iDj*^vA|N#)y^C}VL`6ZV0s@LkklsTNJ<F0GaduXAb~aWvHV#gn6C9k} zoNR0-PM+ZA<>Tk)XXg?SJjo}>!^h9}=S%45fzL27vM@5T@Nuwl@cox>v~M8pj5$?Vkbv^+tCL7$Xz&aTZoK;DYKCpks9O^v4+J|BM>A 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z5yj+&bP;EfL1FlKj$#iBm#18>>rM<$NgIVAJwk=>;M1& From 6bba55a6fb75cd286d18808d874bdf13327f1df3 Mon Sep 17 00:00:00 2001 From: Djura Smits Date: Tue, 30 Jul 2024 16:27:45 +0200 Subject: [PATCH 4/8] Fixed classic analysis pic --- episodes/chap1_introduction_FL.md | 2 +- episodes/fig/chapter1/classic_analysis.jpg | Bin 0 -> 49825 bytes 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 episodes/fig/chapter1/classic_analysis.jpg diff --git a/episodes/chap1_introduction_FL.md b/episodes/chap1_introduction_FL.md index 6d7d5703..a3bcaf8a 100644 --- a/episodes/chap1_introduction_FL.md +++ b/episodes/chap1_introduction_FL.md @@ -38,7 +38,7 @@ In classic data analysis, all data is copied over into a single place. 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j1AqCzUq0}c5B%i=fBC>)KJb?h{N)4x_xJ$%=kWgkpVMPV literal 0 HcmV?d00001 From 138ccac25ca582880ff0342044c4a7a825146515 Mon Sep 17 00:00:00 2001 From: Djura Smits Date: Tue, 30 Jul 2024 16:48:16 +0200 Subject: [PATCH 5/8] Fixed pic --- episodes/chap1_introduction_FL.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/chap1_introduction_FL.md b/episodes/chap1_introduction_FL.md index a3bcaf8a..48693844 100644 --- a/episodes/chap1_introduction_FL.md +++ b/episodes/chap1_introduction_FL.md @@ -74,7 +74,7 @@ will in this case be removed so that individuals stay anonymous. Data _pseudonim similar process, but in this case, the records will be assigned an id that will make it possible to link individuals across datasets. -![Data anonymization](fig%2Fdata_anonymization.jpg){alt='Names are censored before the datasets +![Data anonymization](fig/chapter1/data_anonymization.jpg){alt='Names are censored before the datasets are sent to a central place'} While data anonymization and pseudonymization are often a good first step, there is no guarantee From fb5a92977fd5febe6af3503649fc2285fb4b40f0 Mon Sep 17 00:00:00 2001 From: Djura Smits Date: Mon, 12 Aug 2024 10:06:07 +0200 Subject: [PATCH 6/8] Made federated learning more clear Made arrows more thicccc --- episodes/chap1_introduction_FL.md | 9 +++++---- episodes/fig/chapter1/classic_analysis.jpg | Bin 49825 -> 51664 bytes episodes/fig/chapter1/data_anonymization.jpg | Bin 46665 -> 48571 bytes .../fig/chapter1/differential privacy.jpg | Bin 45272 -> 0 bytes .../fig/chapter1/differential_privacy.jpg | Bin 0 -> 47555 bytes episodes/fig/chapter1/federated learning.jpg | Bin 47993 -> 0 bytes episodes/fig/chapter1/federated sum.jpg | Bin 46014 -> 0 bytes episodes/fig/chapter1/federated_learning.jpg | Bin 0 -> 65533 bytes episodes/fig/chapter1/federated_sum.jpg | Bin 0 -> 48315 bytes episodes/fig/chapter1/mpc.jpg | Bin 55069 -> 57832 bytes 10 files changed, 5 insertions(+), 4 deletions(-) delete mode 100644 episodes/fig/chapter1/differential privacy.jpg create mode 100644 episodes/fig/chapter1/differential_privacy.jpg delete mode 100644 episodes/fig/chapter1/federated learning.jpg delete mode 100644 episodes/fig/chapter1/federated sum.jpg create mode 100644 episodes/fig/chapter1/federated_learning.jpg create mode 100644 episodes/fig/chapter1/federated_sum.jpg diff --git a/episodes/chap1_introduction_FL.md b/episodes/chap1_introduction_FL.md index 48693844..d106084e 100644 --- a/episodes/chap1_introduction_FL.md +++ b/episodes/chap1_introduction_FL.md @@ -93,12 +93,13 @@ over it. In this case, it is not the data that travels, but the analysis itself. a query or instruction to the data and only the results will get back to the user. The results are often akin to a form of _aggregation_ of the data. This can be in the shape of traditional -statistics like the mean, or it can be more intricate like a machine learning model. +statistics like the mean, or it could be a combination of aggregations to form a more complicated +analysis. Aggregating the data does not ensure complete protection of person-level information, but it certainly makes it less likely that this will happen. -![General flow of a federated analysis](fig/chapter1/federated%20sum.jpg) +![In federated analysis, an aggregation of the local data is sent to a central point. At the central point the local aggregations are combined. This can also be a small step in a larger analysis.](fig/chapter1/federated_sum.jpg) TODO: Example of data leakage in simple aggregated case @@ -111,7 +112,7 @@ traditional federated learning, the clients train machine learning models, and o the models are sent back to the central server. The central server combines the updates from all the individual clients into one final machine learning model. -![In federated learning only the gradients of models are shared](fig/chapter1/federated%20learning.jpg) +![In federated learning only the gradients of models are shared](fig/chapter1/federated_learning.jpg) There are caveats to using this type of data analysis though. Although the data transmitted from the clients to the server are an aggregation of the raw data, researchers have found a way to use this @@ -179,7 +180,7 @@ selection of inputs with random noise. A single individual will then always be a their data has contributed to the final result. An individual has _plausible deniability_ with regards to whether it was part of the dataset. -![Differential privacy replaces a subset of the data with random values](fig/chapter1/differential%20privacy.jpg) +![Differential privacy replaces a subset of the data with random values](fig/chapter1/differential_privacy.jpg) ## Blocks upon blocks diff --git a/episodes/fig/chapter1/classic_analysis.jpg b/episodes/fig/chapter1/classic_analysis.jpg index 727a38d1db4b2356066e10b8082462e735282061..0b3d9d3681e534846cae7d5beaad6417a2ec732f 100644 GIT binary patch delta 32264 zcmb4rc|4Tw_xF@ak){%|PNgV9$yS!Bgd{0tUnWa-CS)ITS3>qm@j;m)*-f&>G-DT% zeP0HXeVG}{7-oC!KFjaMO$) z-6L^~4)vQ9h4X$(a})aHo>qxHzIMB=+T=*=Q`PfcSGa#+a~5SAY>#wl*w}k)jGbt< zDCI!Xg=j68&^Pzyeo(uclVz;Y_SrtSw@|7_>t6R zh@&+2_|CYho|FO>-=@8WKG=yzQJb0k1`XQ*DGK2lM%jyZ4(Q-Nh+4KKT-M!?9$m&F zlI0t~*KI7II1nvlIQ>-)0oJqXJZDnYtC}>giObbc~}uhaTk< zW_Hud|Ir;CpLZV;y8=2AWmFFXAb*VA=yAeMG%ve_y@!H~#rd-0rVLe=iSA$6-wBwu 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insertion(+), 1 deletion(-) diff --git a/episodes/chap1_introduction_FL.md b/episodes/chap1_introduction_FL.md index d106084e..e21f15f8 100644 --- a/episodes/chap1_introduction_FL.md +++ b/episodes/chap1_introduction_FL.md @@ -118,7 +118,7 @@ There are caveats to using this type of data analysis though. Although the data clients to the server are an aggregation of the raw data, researchers have found a way to use this data to reconstruct the original data. This vulnerability is called _gradient leakage_. -![An example of gradient leakage](fig/chapter1/gradient_leakage.jpg) +![An example of gradient leakage. The order might not be correct but the images are still very close to the originals.](fig/chapter1/gradient_leakage.jpg) ## Secure Multiparty Computation From ee193edd090ba256d34a518159506a11c42c3a55 Mon Sep 17 00:00:00 2001 From: Djura Smits Date: Mon, 12 Aug 2024 10:22:12 +0200 Subject: [PATCH 8/8] Added third dataset to mpc --- episodes/fig/chapter1/mpc.jpg | Bin 57832 -> 64014 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/episodes/fig/chapter1/mpc.jpg b/episodes/fig/chapter1/mpc.jpg index 77b3c771cc202e951524d7d7d923894731e3b9ee..401103782dc4aaa6bdf37b9ec54e0fdce9b5fa74 100644 GIT binary patch delta 35744 zcmZ^~cTiJ*^eq}iMX5^fQ9(fv5TutxRJw?ufYhj%PD9}I`gC+F`BYH`AFC^X*3&jO|J+{cCB@u}58fyp_cvIz7+Onw1f;B75Ewov~N^b zXSq=X?-^Ls=j?QI*b%r|7dw3rniXk*oqr6cGr-6W+{y2q+;kjEMtM`x>v>6vL~Yp} zPAeUiW0e$Dxwdmvi9dn&T-}VVwy|0?f%u1Bg9p)F^``nbD&KqD>?Kvxz zcG2+NJ0?aS3TlevivDwpbPpw~6!j^mZ;>|KNXfm9Q^@zl zQfKPrRWEKS)sfPzpZ8i0XphHS)Xnm5l-+et7hm-Dm}@d#o?Q1kx!u2XKzm{=KG&@3 zGY5N8S-Zdcx9JoDbogN%GAfMzMObmfts>q%qeviFi1Y~as1Jt#1_M$4sUL`UN|L8c-Xhz3qLoh5;U}QvykI_TXxaX$b)NC z8+3e`9U-HKa9AvdY(OQ^oI+GgEWPGum`E_^M2EQHtd{`uzv?1$+^5>}?WpqV3h-+g zuAD;d_wWr{ttv`gnKZd&i5@^Ot4MyZK80N3sG~$wF`%-mt759y+tG`aw1ROTt-=>h zAyF76vQfF6m3+VRvh(MXbe7+z5EKXFiAf;pAtinv2;tE+)6bK6FY$%KzmyPr{e^~iC`D@&v7x5V1t`XbPGAs1swc?%(Z{{u7(&o0_ zZFqTZ2R7(uk;&}!vCRqOZpDV$Em%k2eZXQEIEB1+JB3^*NIdxK)=iy01UfQBP9avF zaML_&k0dkDq)EMDm-6)KkUReysiUN=<)Ej;%q{3qkB>_*Za&w{J;lTle`BQTC~IhE z)R7eE{s@>e>Qlk8^JfAY9z(tOr?bWfk4XMu$JmxR{1e0{v(dSV+4b5h&XYlDJ<*Ry zyEa%oFd4V_@whf#4a`!{rJ2S}w^<<;Xh!EVYJ8iDtPjA(b1V?4@`eZ7WB^ZE-G9d6 zH98qs+h^y>{)cf;%R&j0&aUdl{}D_mKESk5h;TE0o^s(7Vm>BI?<=PDBd|mQ>%4rW zjlo{p^y@x5XE@3{6Up47lo*0~kZ-=mb2EkB!g9_MHoh3)Aw>0K(!R^G$EL}%uU|{`oC`y5##H_J6mh3>`LenXKUx}PNE9Q;Q!nWftaXUN^Nczxt)~!X zNphWeN3C9I&yYv)B9=~jdZ1IubNTXI9;OVjb!+|@y zX}G4#J?IpZfBNmr`+bvjCI+OQ*{eY+0;doq2Df9AQV6TP`kiwf{gfj5qLr@PE5m%623qkpWOIW9+AF-A&#gKB#wyronXX$Y(CfJsnj*g)w z+rMmuL(Mg6KItr(a||XngHjtt$==v5^Kd(yaC%+m_U7n(6*t4x`B-n!$3v9KlWWgJ z{VqU&`7;Q4bT_U-IHD1bj5Gc*MrCmjfDCOqomz=hqV)-IatZe_r_W5>5G^A- zjmtEH9y}t&7V=j3S4`940UNrWgLL@0k#VolnBK);O6rU?c)Nx9XLZvN`~IA zT%IVr2*1XFh)1^%KbUpakZD-cd8y96huz{NSuh8}M2G6n%ttd?pdJv@^RFiG)AbPe zix7K*3++NE?nD#3nSUBjvYTN#?7FpI)Ebd-R_S-5m3< zH1_oGc;x?*1OJ^f=3798iP(7Tb7n(j{fWIL^~A07_{4{Vo}d)Btfu_ieiJj7dw2zZ z-eFL98Lyz?I&Sa>`*Kfg_dt~cEMQ52}ZDEv)RsUc!Ss|v7QKM}!xhADz* zsv?VbSZb?k(W;jO7P*(wX6FnWD!*QeqZfh=f>Koa1guv zP>y(gAW$53Z5G8+H3;LIwGfi~xn5ON6HMe|E#&gu@Lesz_Bpe%)74xEl36V2*u?7;NcL!R$-( z+~c9*th$=7`&yazx99vW6i{^aWZe-NjZY}?V3Bd5@E&FnQ;U(BdibdHO!3rjiHApJ z)3-C88$Q%ey~le=S`t!~ zS>qYCrpm-`+L1o#aFBLssWwP4!&S&o#NA`{SA@UVUM7`Zc%Hbe zgW|>Swb5a4WsTKCseaT;)+XHKSMVfkA?{Tl6yA!Na9fxylkwNh9%AWQD2ui6`>G9F z>1I5IoGr4>`u5ldz4@1Q3XGre)wmxz>_A2C91QbL$jT&BGOwWA(Edb9Cb>+x`i*W# z!b9blAhnFQ*43dI39<=tTs9u(uB@sRNitfI`y4x>>oLP7Y8EWfVx`N&!9w32Gq=Le zp!tx6t|KbU-={lg-jL4O-8zMQ%ws$mOF{-5(h^lmoaIhr55u-b$|?8t`?V~XyMV^f zjEbP^74RS@R4?6E6;(u3G9ZL{y2wnt2@7zoUPa3f7Xh`kf#1B~A4ROGM*pm?fuZ1t zQX4vg#g(iom0M^07OLEZ{z^xXeQ;W5$Vx2Ca^&2|a0n%9Y-RH>aA}9hGSw-+F~3Zb40dJtMA}51ptTs0>Eq3-f(IaC-Ew9?XmL zY($enCr-kK5AP}DO6^aUt0!EkzoCI3sX2w%yL@UCo;s8iSS2K^Rt@pq_bt7ngU4M4 z|NAcLnpZWjnkGJ4-t0p^o>6N6(!7vfIM1HsWuWOU`L$j3YNObIhb;g3MP2(=U&v=$ zLgx725g1Ptd@Ofevgpk|T1iK@n+n~BtL+!N>5UbWDe_M$hF_jS(h|yU7ar%!`rt$k zT*k3WwgD?7z1-lm)sDdxZCq#yp0|Y>uS;c>1SWYGOvorJ;)-^VM8Ff>(5p1#I^0tG znIvgeD!WY!$vzvtwD1?Y(Ia?}Q-)Gs6u;Z-BimbCWYv;uSNV&(q=eVe)xSy2dfX<; 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