Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32p performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.

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TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android's virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.

/pdf/taintdroid-an-information-flow-tracking-system-for-realtime-3fnz9lwdso.pdf

TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

Ordered Binary-Decision Diagrams (OBDDs) represent Boolean functions as directed acyclic graphs. They form a canonical representation, making testing of functional properties such as satisfiability and equivalence straightforward. A number of operations on Boolean functions can be implemented as graph algorithms on OBDD data structures. Using OBDDs, a wide variety of problems can be solved through symbolic analysis. First, the possible variations in system parameters and operating conditions are encoded with Boolean variables. Then the system is evaluated for all variations by a sequence of OBDD operations. Researchers have thus solved a number of problems in digital-system design, finite-state system analysis, artificial intelligence, and mathematical logic. This paper describes the OBDD data structure and surveys a number of applications that have been solved by OBDD-based symbolic analysis.

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Symbolic Boolean manipulation with ordered binary-decision diagrams

Physical Unclonable Functions (PUFs) are innovative circuit primitives that extract secrets from physical characteristics of integrated circuits (ICs). We present PUF designs that exploit inherent delay characteristics of wires and transistors that differ from chip to chip, and describe how PUFs can enable low-cost authentication of individual ICs and generate volatile secret keys for cryptographic operations.

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Physical unclonable functions for device authentication and secret key generation

Physical Unclonable Functions for Device Authentication and Secret Key Generation

This paper proposes a solution to address the issue of authentication to prevent counterfeiting in a low cost RFID based system based on using a lightweight primitive, Physically Unclonable Functions.

A Low Cost Solution to Cloning and Authentication Based on a Lightweight Primitive

Iterative collision search procedures play a key role in developing combinatorial algorithms for the subset sum and learning parity with noise (LPN) problems. In both scenarios, the single-list pair-wise iterative collision search finds the most solutions and offers the best efficiency. However, due to its complex probabilistic structure, no rigorous analysis for it appears to be available to the best of our knowledge. As a result, theoretical works often resort to overly constrained and sub-optimal iterative collision search variants in exchange for analytic simplicity. In this paper, we present rigorous analysis for the single-list pair-wise iterative collision search method and its applications in subset sum and LPN. In the LPN literature, the method is known as the LF2 heuristic. Besides LF2, we also present rigorous analysis of other LPN solving heuristics and show that they work well when combined with LF2. Putting it together, we significantly narrow the gap between theoretical and heuristic algorithms for LPN.

On Iterative Collision Search for LPN and Subset Sum

We address the problem of verifying that the gate-level implementation of an asynchronous circuit, with given or extracted bounds on wire and gate delays, is equivalent to a specification of the asynchronous circuit behavior described as a classical flow table. We give a procedure to extract the complete set of possible flow tables from a gate-level description of an asynchronous circuit under the bounded wire delay model. Given an extracted flow table and the initial flow table specification, we give procedures to construct a product flow table so as to check for machine equivalence under various modes of operation.

Verification of asynchronous interface circuits with bounded wire delays

Privacy concerns with sensitive data are receiving increasing attention. In this paper, we study local differential privacy (LDP) in interactive decentralized optimization. By constructing random local aggregators, we propose a framework to amplify LDP by a constant. We take Alternating Direction Method of Multipliers (ADMM), and decentralized gradient descent as two concrete examples, where experiments support our theory. In an asymptotic view, we address the following question: Under LDP, is it possible to design a distributed private minimizer for arbitrary closed convex constraints with utility loss not explicitly dependent on dimensionality? As an affiliated result, we also show that with merely linear secret sharing, information theoretic privacy is achievable for bounded colluding agents.

Local Differential Privacy in Decentralized Optimization

We show that the problem of factoring a sum-of-products representation of a logic function can be transformed into one of multiple-valued prime generation followed by branch-and-bound covering. We give a factorization method that generates potential Boolean factors by generating the primes of a multiple-valued function with an associated don't-care set. A covering problem is solved wherein a set of primes with minimal cost is selected to obtain a Boolean factorization. This method can exploit Boolean identifiers in factorization such as a-a = a-a = a. Common factors across a set of Boolean functions can be identified by using multiple-output prime generation and covering. We show how all the kernels of an expression can be generated by generating the primes of a multiple-valued function. A covering step can be used to arrive at an algebraic factorization.

Srinivas Devadas

Papers

A Low Cost Solution to Cloning and Authentication Based on a Lightweight Primitive

On Iterative Collision Search for LPN and Subset Sum

Verification of asynchronous interface circuits with bounded wire delays

Local Differential Privacy in Decentralized Optimization

Boolean factorization using multiple-valued minimization