Shohei Kakei

The utilization of personal data can create new insights. On the other hand, data providers have concerns about unauthorized acquisition and misuse of provided data. We propose a decentralized data processing infrastructure, named VDDPI, that enables data providers to verify data processing implemented by data consumers. VDDPI manages information on data processing apps using distributed ledger technology, and data providers can obtain this information and check whether the provided data will be disclosed or not before providing data. Furthermore, by running the app using trusted execution environments (TEEs), the integrity of the app and the confidentiality of the data provided are ensured. Expanding beyond data processing verification, VDDPI provides decentralized data usage control, allowing continuous control over the number of uses, location, date and time of use. We evaluated whether VDDPI meets security requirements derived from STRIDE and performed formal verification using ProVerif. In addition, we implemented a prototype using Intel SGX and Hyperledger Fabric and compared its performance with related work. The results of the performance evaluation confirmed that the data processing time was approximately equivalent to related work.

Major app stores have introduced privacy labels (e.g., Google Play's data safety section since July 2022), requiring app developers to provide their privacy disclosures, including data types collected and shared by their apps and third-party SDKs they use. Third-party SDK providers have published guidance pages instructing app developers what data types their SDKs use and thus must be declared to the data safety section. Availability and correctness of the guidance pages are critical issues but have yet to receive any attention. This paper presents the first study of the guidance pages. We first attempted to collect the guidance pages of 175 commercial SDKs widely used in Android apps and did not obtain them for 63% of the SDKs, suggesting that the majority of them have not provided guidance pages. Further, we develop a system that detects inconsistencies between the guidance pages and the actual data collection of SDKs. It uses machine learning and dynamic taint analysis to extract privacy practices from the guidance pages and SDKs and analyzes the outcomes to detect the critical gap. We construct datasets of 47 guidance pages and 43 SDKs' 159 sample apps and evaluate the system. The system uncovered discrepancies related to location and identifiers in the guidance pages of eight SDKs. We also evaluate the machine learning model's accuracy for unknown guidance page contents. The results show that the model performs satisfactorily for updated guidance pages, and the accuracy for newly posted ones increases as the model learns more. This study exposes the critical issues of the guidance pages and also contributes to tools and datasets for facilitating further research on guidance pages and privacy labels.

This paper first takes IoT as an example to provide the motivation for eliminating the single point of trust (SPOT) in a CA-based private PKI. It then describes a distributed public key certificate-issuing infrastructure that eliminates the SPOT and its limitation derived from generating signing keys. Finally, it proposes a method to address its limitation by all certificate issuers.

With the development of cloud services and the Internet of Things, the integration of heterogeneous systems is becoming increasingly complex. Identity management is important in the coordination of various systems, and public key infrastructure (PKI) is widely known as an identity management methods. In PKI, a certificate authority (CA) acts as a trust point to guarantee the identity of entities such as users, devices, and services. However, traditional CAs that delegate the operations to a specific organization are not always suitable for heterogeneous services, and a new methodology is required to enable multiple stakeholders to securely and cooperatively operate a CA. In this study, we introduce the concept of a consortium CA and propose a distributed public key certificate-issuing infrastructure that realizes a consortium CA. The proposed infrastructure enables multiple organizations to cooperatively operate a CA suitable for services involving multiple stakeholders. We identify four requirements for the cooperative operation of a consortium CA and design the proposed infrastructure with distributed ledger technology. Furthermore, we present the implementation of smart contracts with Hyperledger Fabric and prove that the proposed infrastructure satisfies the four requirements. Finally, we confirm that certificate issuance and verification are stable at approximately 4 and 3 ms, respectively.

Security researchers utilize taint analyses to uncover suspicious behaviors in Android apps. Current static taint analyzers cannot handle ICC, reflection, and lifecycles dependably, increasing the result verification cost. On the other hand, current dynamic taint trackers accurately detect execution paths. However, they depend on specific Android versions and modified devices, reducing their usability and applicability. In addition, they require app exercise every time running the taint analysis. This paper presents a new dynamic taint tracker called T-Recs, tracking information flows by recording and reconstructing the app execution. First, before the taint analysis, the app’s runtime data are obtained by instrumenting logging code into the app’s bytecode and running the app to be independent of specific Android versions and devices. Then, T-Recs performs the taint analysis accurately with the logged data and separately from the app exercise. This paper is an extended version of our work published. Previously, T-Recs’ accuracy was mainly evaluated in privacy leak detection. The results show that T-Recs outperforms compared analyzers, which are FlowDroid (w/ and w/o IC3), Amandroid, DroidSafe, and TaintDroid (w/ and w/o IntelliDroid). This paper also involves DroidRA and IccTA. This paper shows that T-Recs detects ICC- and reflection-related leaks missed by FlowDroid in popular Google Play apps. The other static analyzers fail to analyze most of the apps. These experiments also indicate an advantage of T-Recs: its users can re-execute T-Recs’ taint analysis without re-exercising the app. T-Recs’ app-runtime overhead and parallel execution performance were also evaluated, and the results are acceptable.