Private BitTorrent trackers enforce upload-to-download ratios to prevent free-riding, but suffer from three critical weaknesses: reputation cannot move between trackers, centralized servers create single points of failure, and upload statistics are self-reported and unverifiable. When a tracker shuts down, users lose their contribution history and cannot prove their standing to new communities. We address these problems by storing reputation in smart contracts and replacing self-reports with cryptographic attestations. Peers sign receipts for received pieces; the tracker aggregates them via BLS signatures and updates reputation. If a tracker is unavailable, peers fall back to an authenticated distributed hash table (DHT): stored reputation acts as a public key infrastructure (PKI), preserving access control without the tracker. Reputation is portable across tracker failures through single-hop migration in factory-deployed contracts. We also address the privacy implications of publishing public keys and reputations tied to private trackers on a public ledger: we propose ephemeral session keys to prevent linking peer identities, zero-knowledge membership proofs for anonymous DHT participation, and confidential reputation using homomorphic commitments. We formalize the security requirements, prove four security properties under standard cryptographic assumptions, and evaluate a prototype. Measurements show that transfer receipts add less than 5\% end-to-end overhead with typical piece sizes. To minimize signing overhead, we adopt a hybrid signature scheme: ECDSA signs individual piece receipts at transfer time for low per-operation latency, while BLS serves as the overarching scheme, enabling compact aggregation of many receipts into a single proof at report time. This design reduces client-side signing cost by an order of magnitude compared to using BLS throughout.

Identifying sensitive user inputs is a prerequisite for privacy protection in mobile applications. When it comes to today's program analysis systems, however, only those data that go through well-defined system Application Program Interface (system controlled resources) can be automatically labeled. In this paper, we show that this conventional approach is far from adequate, as most sensitive inputs are actually entered by the user at an app's runtime. In this paper, we inspect 13,072 top apps from Google Play, and find that 38.69% of them involve sensitive user inputs. Just like system controlled resources, these data are also exposed to a series of privacy leakage threats. For these sensitive user inputs, manually marking them involves a lot of efforts, impeding a large-scale, automated analysis of apps to defend against potential privacy leakage. To address this important issue, we present UIPicker, an adaptable framework for automatic identification of sensitive user inputs as the first step. UIPicker is designed to detect the semantic information within the application layout resources and the program code, and further analyze it for the locations where security-critical information may show up. This approach can support a variety of existing security analysis on mobile apps. We evaluate our approach over randomly selected popular apps on Google Play. UIPicker is able to accurately label sensitive user inputs most of the time, with 94.0% precision and 96.0% recall.