iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.48550/arXiv.2205.06911
[2205.06911] Blockaid: Data Access Policy Enforcement for Web Applications
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Databases

arXiv:2205.06911 (cs)
[Submitted on 13 May 2022 (v1), last revised 1 Jun 2022 (this version, v2)]

Title:Blockaid: Data Access Policy Enforcement for Web Applications

Authors:Wen Zhang, Eric Sheng, Michael Chang, Aurojit Panda, Mooly Sagiv, Scott Shenker
View PDF
Abstract:Modern web applications serve large amounts of sensitive user data, access to which is typically governed by data-access policies. Enforcing such policies is crucial to preventing improper data access, and prior work has proposed many enforcement mechanisms. However, these prior methods either alter application semantics or require adopting a new programming model; the former can result in unexpected application behavior, while the latter cannot be used with existing web frameworks.
Blockaid is an access-policy enforcement system that preserves application semantics and is compatible with existing web frameworks. It intercepts database queries from the application, attempts to verify that each query is policy-compliant, and blocks queries that are not. It verifies policy compliance using SMT solvers and generalizes and caches previous compliance decisions for better performance. We show that Blockaid supports existing web applications while requiring minimal code changes and adding only modest overheads.
Comments: Extended technical report for OSDI 2022 paper
Subjects: Databases (cs.DB)
Cite as: arXiv:2205.06911 [cs.DB]
  (or arXiv:2205.06911v2 [cs.DB] for this version)
  https://doi.org/10.48550/arXiv.2205.06911

Submission history

From: Wen Zhang [view email]
[v1] Fri, 13 May 2022 22:13:15 UTC (660 KB)
[v2] Wed, 1 Jun 2022 01:59:02 UTC (994 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
cs.DB
< prev   |   next >
new | recent | 2022-05
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)