Petter Reinholdtsen

I dag fikk jeg en skikkelig gladmelding. Bakgrunnen er at før jul arrangerte Nasjonalbiblioteket et seminar om sitt knakende gode tiltak «verksregister». Eneste måten å melde seg på dette seminaret var å sende personopplysninger til Google via Google Skjemaer. Dette syntes jeg var tvilsom praksis, da det bør være mulig å delta på seminarer arrangert av det offentlige uten å måtte dele sine interesser, posisjon og andre personopplysninger med Google. Jeg ba derfor om innsyn via Mimes brønn i avtaler og vurderinger Nasjonalbiblioteket hadde rundt dette. Personopplysningsloven legger klare rammer for hva som må være på plass før en kan be tredjeparter, spesielt i utlandet, behandle personopplysninger på sine vegne, så det burde eksistere grundig dokumentasjon før noe slikt kan bli lovlig. To jurister hos Nasjonalbiblioteket mente først dette var helt i orden, og at Googles standardavtale kunne brukes som databehandlingsavtale. Det syntes jeg var merkelig, men har ikke hatt kapasitet til å følge opp saken før for to dager siden.

Gladnyheten i dag, som kom etter at jeg tipset Nasjonalbiblioteket om at Datatilsynet underkjente Googles standardavtaler som databehandleravtaler i 2011, er at Nasjonalbiblioteket har bestemt seg for å avslutte bruken av Googles Skjemaer/Apps og gå i dialog med DIFI for å finne bedre måter å håndtere påmeldinger i tråd med personopplysningsloven. Det er fantastisk å se at av og til hjelper det å spørre hva i alle dager det offentlige holder på med.

Jeg leste med interesse en nyhetssak hos digi.no og NRK om at det ikke bare er meg, men at også NAV bedriver geolokalisering av IP-adresser, og at det gjøres analyse av IP-adressene til de som sendes inn meldekort for å se om meldekortet sendes inn fra utenlandske IP-adresser. Politiadvokat i Drammen, Hans Lyder Haare, er sitert i NRK på at «De to er jo blant annet avslørt av IP-adresser. At man ser at meldekortet kommer fra utlandet.»

Jeg synes det er fint at det blir bedre kjent at IP-adresser knyttes til enkeltpersoner og at innsamlet informasjon brukes til å stedsbestemme personer også av aktører her i Norge. Jeg ser det som nok et argument for å bruke Tor så mye som mulig for å gjøre gjøre IP-lokalisering vanskeligere, slik at en kan beskytte sin privatsfære og unngå å dele sin fysiske plassering med uvedkommede.

Men det er en ting som bekymrer meg rundt denne nyheten. Jeg ble tipset (takk #nuug) om NAVs personvernerklæring, som under punktet «Personvern og statistikk» lyder:

«Når du besøker nav.no, etterlater du deg elektroniske spor. Sporene dannes fordi din nettleser automatisk sender en rekke opplysninger til NAVs tjener (server-maskin) hver gang du ber om å få vist en side. Det er eksempelvis opplysninger om hvilken nettleser og -versjon du bruker, og din internettadresse (ip-adresse). For hver side som vises, lagres følgende opplysninger:

• hvilken side du ser på
• dato og tid
• hvilken nettleser du bruker
• din ip-adresse

Ingen av opplysningene vil bli brukt til å identifisere enkeltpersoner. NAV bruker disse opplysningene til å generere en samlet statistikk som blant annet viser hvilke sider som er mest populære. Statistikken er et redskap til å forbedre våre tjenester.»

Jeg klarer ikke helt å se hvordan analyse av de besøkendes IP-adresser for å se hvem som sender inn meldekort via web fra en IP-adresse i utlandet kan gjøres uten å komme i strid med påstanden om at «ingen av opplysningene vil bli brukt til å identifisere enkeltpersoner». Det virker dermed for meg som at NAV bryter sine egen personvernerklæring, hvilket Datatilsynet fortalte meg i starten av desember antagelig er brudd på personopplysningsloven.

I tillegg er personvernerklæringen ganske misvisende i og med at NAVs nettsider ikke bare forsyner NAV med personopplysninger, men i tillegg ber brukernes nettleser kontakte fem andre nettjenere (script.hotjar.com, static.hotjar.com, vars.hotjar.com, www.google-analytics.com og www.googletagmanager.com), slik at personopplysninger blir gjort tilgjengelig for selskapene Hotjar og Google , og alle som kan lytte på trafikken på veien (som FRA, GCHQ og NSA). Jeg klarer heller ikke se hvordan slikt spredning av personopplysninger kan være i tråd med kravene i personopplysningloven, eller i tråd med NAVs personvernerklæring.

Kanskje NAV bør ta en nøye titt på sin personvernerklæring? Eller kanskje Datatilsynet bør gjøre det?

Did you ever wonder where the web trafic really flow to reach the web servers, and who own the network equipment it is flowing through? It is possible to get a glimpse of this from using traceroute, but it is hard to find all the details. Many years ago, I wrote a system to map the Norwegian Internet (trying to figure out if our plans for a network game service would get low enough latency, and who we needed to talk to about setting up game servers close to the users. Back then I used traceroute output from many locations (I asked my friends to run a script and send me their traceroute output) to create the graph and the map. The output from traceroute typically look like this:

traceroute to www.stortinget.no (85.88.67.10), 30 hops max, 60 byte packets
 1  uio-gw10.uio.no (129.240.202.1)  0.447 ms  0.486 ms  0.621 ms
 2  uio-gw8.uio.no (129.240.24.229)  0.467 ms  0.578 ms  0.675 ms
 3  oslo-gw1.uninett.no (128.39.65.17)  0.385 ms  0.373 ms  0.358 ms
 4  te3-1-2.br1.fn3.as2116.net (193.156.90.3)  1.174 ms  1.172 ms  1.153 ms
 5  he16-1-1.cr1.san110.as2116.net (195.0.244.234)  2.627 ms he16-1-1.cr2.oslosda310.as2116.net (195.0.244.48)  3.172 ms he16-1-1.cr1.san110.as2116.net (195.0.244.234)  2.857 ms
 6  ae1.ar8.oslosda310.as2116.net (195.0.242.39)  0.662 ms  0.637 ms ae0.ar8.oslosda310.as2116.net (195.0.242.23)  0.622 ms
 7  89.191.10.146 (89.191.10.146)  0.931 ms  0.917 ms  0.955 ms
 8  * * *
 9  * * *
[...]

This show the DNS names and IP addresses of (at least some of the) network equipment involved in getting the data traffic from me to the www.stortinget.no server, and how long it took in milliseconds for a package to reach the equipment and return to me. Three packages are sent, and some times the packages do not follow the same path. This is shown for hop 5, where three different IP addresses replied to the traceroute request.

There are many ways to measure trace routes. Other good traceroute implementations I use are traceroute (using ICMP packages) mtr (can do both ICMP, UDP and TCP) and scapy (python library with ICMP, UDP, TCP traceroute and a lot of other capabilities). All of them are easily available in Debian.

This time around, I wanted to know the geographic location of different route points, to visualize how visiting a web page spread information about the visit to a lot of servers around the globe. The background is that a web site today often will ask the browser to get from many servers the parts (for example HTML, JSON, fonts, JavaScript, CSS, video) required to display the content. This will leak information about the visit to those controlling these servers and anyone able to peek at the data traffic passing by (like your ISP, the ISPs backbone provider, FRA, GCHQ, NSA and others).

Lets pick an example, the Norwegian parliament web site www.stortinget.no. It is read daily by all members of parliament and their staff, as well as political journalists, activits and many other citizens of Norway. A visit to the www.stortinget.no web site will ask your browser to contact 8 other servers: ajax.googleapis.com, insights.hotjar.com, script.hotjar.com, static.hotjar.com, stats.g.doubleclick.net, www.google-analytics.com, www.googletagmanager.com and www.netigate.se. I extracted this by asking PhantomJS to visit the Stortinget web page and tell me all the URLs PhantomJS downloaded to render the page (in HAR format using their netsniff example. I am very grateful to Gorm for showing me how to do this). My goal is to visualize network traces to all IP addresses behind these DNS names, do show where visitors personal information is spread when visiting the page.

When I had a look around for options, I could not find any good free software tools to do this, and decided I needed my own traceroute wrapper outputting KML based on locations looked up using GeoIP. KML is easy to work with and easy to generate, and understood by several of the GIS tools I have available. I got good help from by NUUG colleague Anders Einar with this, and the result can be seen in my kmltraceroute git repository. Unfortunately, the quality of the free GeoIP databases I could find (and the for-pay databases my friends had access to) is not up to the task. The IP addresses of central Internet infrastructure would typically be placed near the controlling companies main office, and not where the router is really located, as you can see from the KML file I created using the GeoLite City dataset from MaxMind.

I also had a look at the visual traceroute graph created by the scrapy project, showing IP network ownership (aka AS owner) for the IP address in question. The graph display a lot of useful information about the traceroute in SVG format, and give a good indication on who control the network equipment involved, but it do not include geolocation. This graph make it possible to see the information is made available at least for UNINETT, Catchcom, Stortinget, Nordunet, Google, Amazon, Telia, Level 3 Communications and NetDNA.

In the process, I came across the web service GeoTraceroute by Salim Gasmi. Its methology of combining guesses based on DNS names, various location databases and finally use latecy times to rule out candidate locations seemed to do a very good job of guessing correct geolocation. But it could only do one trace at the time, did not have a sensor in Norway and did not make the geolocations easily available for postprocessing. So I contacted the developer and asked if he would be willing to share the code (he refused until he had time to clean it up), but he was interested in providing the geolocations in a machine readable format, and willing to set up a sensor in Norway. So since yesterday, it is possible to run traces from Norway in this service thanks to a sensor node set up by the NUUG assosiation, and get the trace in KML format for further processing.

Here we can see a lot of trafic passes Sweden on its way to Denmark, Germany, Holland and Ireland. Plenty of places where the Snowden confirmations verified the traffic is read by various actors without your best interest as their top priority.

Combining KML files is trivial using a text editor, so I could loop over all the hosts behind the urls imported by www.stortinget.no and ask for the KML file from GeoTraceroute, and create a combined KML file with all the traces (unfortunately only one of the IP addresses behind the DNS name is traced this time. To get them all, one would have to request traces using IP number instead of DNS names from GeoTraceroute). That might be the next step in this project.

Armed with these tools, I find it a lot easier to figure out where the IP traffic moves and who control the boxes involved in moving it. And every time the link crosses for example the Swedish border, we can be sure Swedish Signal Intelligence (FRA) is listening, as GCHQ do in Britain and NSA in USA and cables around the globe. (Hm, what should we tell them? :) Keep that in mind if you ever send anything unencrypted over the Internet.

PS: KML files are drawn using the KML viewer from Ivan Rublev, as it was less cluttered than the local Linux application Marble. There are heaps of other options too.

As usual, if you use Bitcoin and want to show your support of my activities, please send Bitcoin donations to my address 15oWEoG9dUPovwmUL9KWAnYRtNJEkP1u1b.

Do you have a large iCalendar file with lots of old entries, and would like to archive them to save space and resources? At least those of us using KOrganizer know that turning on and off an event set become slower and slower the more entries are in the set. While working on migrating our calendars to a Radicale CalDAV server on our Freedombox server, my loved one wondered if I could find a way to split up the calendar file she had in KOrganizer, and I set out to write a tool. I spent a few days writing and polishing the system, and it is now ready for general consumption. The code for ical-archiver is publicly available from a git repository on github. The system is written in Python and depend on the vobject Python module.

To use it, locate the iCalendar file you want to operate on and give it as an argument to the ical-archiver script. This will generate a set of new files, one file per component type per year for all components expiring more than two years in the past. The vevent, vtodo and vjournal entries are handled by the script. The remaining entries are stored in a 'remaining' file.

This is what a test run can look like:

% ical-archiver t/2004-2016.ics 
Found 3612 vevents
Found 6 vtodos
Found 2 vjournals
Writing t/2004-2016.ics-subset-vevent-2004.ics
Writing t/2004-2016.ics-subset-vevent-2005.ics
Writing t/2004-2016.ics-subset-vevent-2006.ics
Writing t/2004-2016.ics-subset-vevent-2007.ics
Writing t/2004-2016.ics-subset-vevent-2008.ics
Writing t/2004-2016.ics-subset-vevent-2009.ics
Writing t/2004-2016.ics-subset-vevent-2010.ics
Writing t/2004-2016.ics-subset-vevent-2011.ics
Writing t/2004-2016.ics-subset-vevent-2012.ics
Writing t/2004-2016.ics-subset-vevent-2013.ics
Writing t/2004-2016.ics-subset-vevent-2014.ics
Writing t/2004-2016.ics-subset-vjournal-2007.ics
Writing t/2004-2016.ics-subset-vjournal-2011.ics
Writing t/2004-2016.ics-subset-vtodo-2012.ics
Writing t/2004-2016.ics-remaining.ics
%

As you can see, the original file is untouched and new files are written with names derived from the original file. If you are happy with their content, the *-remaining.ics file can replace the original the the others can be archived or imported as historical calendar collections.

The script should probably be improved a bit. The error handling when discovering broken entries is not good, and I am not sure yet if it make sense to split different entry types into separate files or not. The program is thus likely to change. If you find it interesting, please get in touch. :)

As usual, if you use Bitcoin and want to show your support of my activities, please send Bitcoin donations to my address 15oWEoG9dUPovwmUL9KWAnYRtNJEkP1u1b.