Canonical 推出的 Dqlite (High-Availability SQLite)

第一眼看到的時候直接有種不知道 Canonical 在幹什麼的感覺,翻完說明後大概知道可以用的地方,但還是覺得範圍有點小:「Dqlite - High-Availability SQLite」。

一種使用情境是,在 embedded system 上面同步資料的一種方案... 吧?例如網路連線的頻寬或是品質受限,無法順利傳到 Internet 端的伺服器上,所以希望在本地端就可以解決一些事情,但又不方便在本地端直接弄個 PostgreSQL 出來?

Dqlite is a fast, embedded, persistent SQL database with Raft consensus that is perfect for fault-tolerant IoT and Edge devices.

另外一個是用到了 C 實做的 Raft 協定:

Dqlite (“distributed SQLite”) extends SQLite across a cluster of machines, with automatic failover and high-availability to keep your application running. It uses C-Raft, an optimised Raft implementation in C, to gain high-performance transactional consensus and fault tolerance while preserving SQlite’s outstanding efficiency and tiny footprint.

讓 IoT 裝置參與 Raft 嗎... 好像只能說有趣... XD

OAuth 2.0 Device Authorization Grant

看到「OAuth 2.0 Device Authorization Grant」這個變成 PROPOSED STANDARD 了,看了一下歷史是 2015 年年底的時候被提出來的,記得在前公司的時候有用這個 (當時還是 draft) 做智慧型電視上的 OAuth 認證:

The OAuth 2.0 device authorization grant is designed for Internet-connected devices that either lack a browser to perform a user-agent-based authorization or are input constrained to the extent that requiring the user to input text in order to authenticate during the authorization flow is impractical. It enables OAuth clients on such devices (like smart TVs, media consoles, digital picture frames, and printers) to obtain user authorization to access protected resources by using a user agent on a separate device.

因為這些裝置的輸入設備受限,照原來 OAuth 2.0 的方式授權,使用者體驗不會太好 (可以想像用遙控器登入 Google 或是 Facebook 帳號?),所以設計了替代的方案,讓使用者可以用手機授權 (比較常見的是透過 QR code),然後電視機再去取得 access token。

Apple 新的「Find My」帶來的隱私問題

這次 WWDC 推出的新功能,已經有人在討論機制與隱私問題了:「How does Apple (privately) find your offline devices?」。

前一代的「Find my iPhone」需要透過網路與 GPS 資料才能在系統上看到,這一代則是加上 BLE beacon,然後任何一台 iOS device 收到後就回傳回給蘋果:

Every active iPhone will continuously monitor for BLE beacon messages that might be coming from a lost device. When it picks up one of these signals, the participating phone tags the data with its own current GPS location; then it sends the whole package up to Apple’s servers.

幾個隱私問題在於,代傳的 iOS device 也會暴露位置資訊給蘋果,另外收到 BLE beacon 的 iOS device 本身是否可以解讀遺失機器的資訊?而商家看起來也可以利用這個方式主動發送攻擊而得知不少資料 (像是文章裡提到先前蘋果透過 randomize mac address 加強隱私的問題,這邊又多開了一個洞),現在蘋果給的資訊還不夠清楚,需要真的逆向工程確認才知道...

利用 Sensor 校正資訊產生 Device Fingerprint 的隱私攻擊

看到「Fingerprinting iPhones」這篇提出的攻擊,標題雖然是提到 iPhone,但實際上攻擊包括了 Android 的手機:

You are affected by this fingerprinting attack if you are using any iOS devices with the iOS version below 12.2, including the latest iPhone XS, iPhone XS Max, and iPhone XR. You are also likely to be affected if you are using a Pixel 2/3 device, although we hypothesise the generated fingerprint has less entropy and is unlikely to be globally unique. A SensorID can be generated by both apps and mobile websites and requires no user interaction.

目前 iPhone 升級到 12.2 之後可以緩解這個問題,Android 看起來還不清楚...

攻擊的方式是透過手機在出場前會使用外部的校正工具,找出手機內 sensor 所偵測到的值與實際值的差異,然後把這些資訊燒到韌體裡,當呼叫 API 時就可以修正給出比較正確的值。

而因為這些校正資訊幾乎每一隻手機都不一樣,而且不會因為重裝而變更 (即使 factory reset),加上還可以跨 app 與 web 追蹤,就成為這次攻擊的目標:

In the context of mobile devices, the main benefit of per-device calibration is that it allows more accurate attitude estimation.

資訊量其實相當大,透過 app 分析可以得到 67 bits entropy,透過網頁也有 42 bits entropy,而且不怎麼會變:

In general, it is difficult to create a unique fingerprint for iOS devices due to strict sandboxing and device homogeneity. However, we demonstrated that our approach can produce globally unique fingerprints for iOS devices from an installed app -- around 67 bits of entropy for the iPhone 6S. Calibration fingerprints generated by a website are less unique (~42 bits of entropy for the iPhone 6S), but they are orthogonal to existing fingerprinting techniques and together they are likely to form a globally unique fingerprint for iOS devices.

We have not observed any change in the SensorID of our test devices in the past half year. Our dataset includes devices running iOS 9/10/11/12. We have tested compass calibration, factory reset, and updating iOS (up until iOS 12.1); the SensorID always stays the same. We have also tried measuring the sensor data at different locations and under different temperatures; we confirm that these factors do not change the SensorID either.

目前提出來的解法是加入隨機值的噪音 (iOS 的作法),不過作者有建議預設應該要關閉 js 存取 sensor 的權限:

To mitigate this calibration fingerprint attack, vendors can add uniformly distributed random noise to ADC outputs before calibration is applied. Alternatively, vendors could round the sensor outputs to the nearest multiple of the nominal gain. Please refer to our paper for more details. In addition, we recommend privacy-focused mobile browsers add an option to disable the access to motion sensors via JavaScript. This could help protect Android devices and iOS devices that no longer receive updates from Apple.

不過當初這群人怎麼會注意到的...

Dropbox 免費版限制三個裝置更新...

Dropbox 決定限制免費版的裝置數量,最多只能有三個裝置同步:「Dropbox adds three-device limit for free users」,對應的頁面是「Is there a limit to the number of devices I can link to my account?」。

既有的裝置不受限,但無法再增加:

If you're a Basic user and you linked more than three devices prior to March 2019, all of your previously linked devices will remain linked, but you can’t link additional devices.

另外一個選擇是付費版,最低是 1TB USD$9.99/month (年繳是 USD$99/year)。

看起來像是養肥了要殺,不過這個領域相關的技術應該是夠成熟,而且也不會用到什麼特別的功能,應該會去看看其他平台的情況,像是 SyncpCloud

其中 Sync 有免費版 (空間限制 5GB,付費版 500GB USD$49/year),不過官方不支援 Linux,有人用 Wine 跑過,但據說穩定性與效能都不太好:「Sync.com in Linux」。

pCloud (500GB EUR$47.88/year) 也是剛剛提到在 Linux 上跑 Sync 的人後來測試的服務,官方有支援 Linux (看起來是透過 AppImage 包裝),也許可以測試看看。

另外一個是自己一直都有在用的 Syncthing,不過設定同步的操作上只有 web interface,而且因為是信任架構,需要多台互相設定,沒那麼方便...

JavaScript Framework 不可避免的成本

看到「The Baseline Costs of JavaScript Frameworks」這篇文章在研究目前主流 JavaScript Framework 無法避免的成本到底有多高。

文章的結論是目前常見的 JavaScript Framework 其實都很肥重,在網路速度不快的地方得花不少時間下載,在非旗艦的手機上會需要花不少時間處理 (parse & compile)。

這是 gzip 後的大小:

這是 parse & compile 的時間:

這是下載時間 (扣除 latency 與 TLS connection 建立時間):

並不是說不能用,但重點會在客群:

But it’s important to consider your audience. If you’re building for resource constrained devices — which you certainly are if your product targets a country like India — you could consider using a lighter framework such as Riot or Preact. Your users will thank you.

最後有建議如果只是要呈現資訊,不要用整套 JavaScript Framework,在有需要互動的地方另外寫就好了:

For websites that primarily display content, it’s more efficient and cost-effective to just send some server-rendered HTML down the wire. If there are areas of your website that require interactivity, you can always use JavaScript to build those specific parts.

Amazon Device Farm 支援讓使用者直接連上去 debug 了...

Amazon Device Farm 推出這樣的功能又朝著設備租賃服務更進一步了:「Amazon Device Farm Launches Direct Device Access for Private Devices」。

Now, with direct device access, mobile applications developers can use individual devices in their private test set as if they were directly connected to their local machine via USB. Developers can now test against a wide array of devices just like they would as if the devices were sitting on their desk.

這樣就可以使用更底層的東西了...

加州的手機防竊提案讓失竊率下降不少...

2013 的時候提過「加州的手機防竊提案...」,後來在 2015 年生效:

In a press release sent to reporters on Thursday, George Gascón said that since the law went into effect on July 1, 2015[,]

在兩大陣營都有類似的功能:

Such a kill switch has become standard in all iPhones ("Activation Lock") and Android phones ("Device Protection") since 2015.

而執行到現在已經兩年了,手機的失竊率下降不少:「San Francisco DA: Anti-theft law results in huge drop in stolen phones」。

[S]martphone-related robberies have fallen 22 percent from 2015 to 2016. When measured from the peak in 2013, "overall robberies involving smartphones have declined an astonishing 50 percent."

變成要找人殺肉才能處理,增加被竊後的處理難度與成本...

透過手機螢幕上的餘熱猜測 PIN 碼

利用手機螢幕上的餘熱分析可能的 PIN 碼:「Heat traces left by fingers can reveal your smartphone PIN」,在輸入完 PIN 碼的 30 秒內的準確度都還是很高 (80%):

The report further revealed that if the thermal image is collected within 15 seconds of a PIN being entered, the technique is accurate almost 90% of the time. At 30 seconds, this accuracy decreased slightly to 80%. At 45 seconds or more, the accuracy dropped to 35% and below.

維基百科的 User Agent 公開資料

Nuzzel 上看到的東西...

維基百科不掛 Google Analytics 之類的第三方服務,而是透過 Piwik 蒐集後自己分析:「Dashboards and Data Downloads for Wikimedia Projects」。

主要有兩個資料可以看,一個是「Browser Statistics」,另外一個是「Readers: Pageviews and Unique Devices」。

不過翻了一下,Piwik 好像還是沒有寫到 NoSQL 之類的方案,出自「How do I use another database like Postgresql, SQLite, Oracle? Will you support Nosql databases like Hadoop, Mongodb?」:

Piwik only works on Mysql, where all the development and testing is done. Supporting multiple databases is a long term objective for Piwik, but not our current focus.

不知道維基百科是怎麼 scale 的...