Statistics for Spatial Data.

The concept of smart grid has emerged as a convergence of traditional power system engineering and information and communication technology. It is vital to the success of next generation of power grid, which is expected to be featuring reliable, efficient, flexible, clean, friendly, and secure characteristics. In this paper, we propose an efficient and privacy-preserving aggregation scheme, named EPPA, for smart grid communications. EPPA uses a superincreasing sequence to structure multidimensional data and encrypt the structured data by the homomorphic Paillier cryptosystem technique. For data communications from user to smart grid operation center, data aggregation is performed directly on ciphertext at local gateways without decryption, and the aggregation result of the original data can be obtained at the operation center. EPPA also adopts the batch verification technique to reduce authentication cost. Through extensive analysis, we demonstrate that EPPA resists various security threats and preserve user privacy, and has significantly less computation and communication overhead than existing competing approaches.

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EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications

Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems (VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. Hidden voice commands, though "hidden", are nonetheless audible. In this work, we design a totally inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers (e.g., f > 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low-frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validated DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to switch the phone to the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions, and suggest to re-design voice controllable systems to be resilient to inaudible voice command attacks.

https://dl.acm.org/doi/pdf/10.1145/3133956.3134052

DolphinAttack: Inaudible Voice Commands

The wireless body area network has emerged as a new technology for e-healthcare that allows the data of a patient's vital body parameters and movements to be collected by small wearable or implantable sensors and communicated using short-range wireless communication techniques. WBAN has shown great potential in improving healthcare quality, and thus has found a wide range of applications from ubiquitous health monitoring and computer assisted rehabilitation to emergency medical response systems. The security and privacy protection of the data collected from a WBAN, either while stored inside the WBAN or during their transmission outside of the WBAN, is a major unsolved concern, with challenges coming from stringent resource constraints of WBAN devices, and the high demand for both security/privacy and practicality/usability. In this article we look into two important data security issues: secure and dependable distributed data storage, and fine-grained distributed data access control for sensitive and private patient medical data. We discuss various practical issues that need to be taken into account while fulfilling the security and privacy requirements. Relevant solutions in sensor networks and WBANs are surveyed, and their applicability is analyzed.

Data security and privacy in wireless body area networks

Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction.

Mobile Phone Sensing Systems: A Survey

People-centric urban sensing is a new paradigm gaining popularity. A main obstacle to its widespread deployment and adoption are the privacy concerns of participating individuals. To tackle this open challenge, this paper presents the design and evaluation of PriSense, a novel solution to privacy-preserving data aggregation in people- centric urban sensing systems. PriSense is based on the concept of data slicing and mixing and can support a wide range of statistical additive and non-additive aggregation functions such as Sum, Average, Variance, Count, Max/Min, Median, Histogram, and Percentile with accurate aggregation results. PriSense can support strong user privacy against a tunable threshold number of colluding users and aggregation servers. The efficacy and efficiency of PriSense are confirmed by thorough analytical and simulation results.

PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems

Proximity-based mobile social networking (PMSN) refers to the social interaction among physically proximate mobile users directly through the Bluetooth/WiFi interfaces on their smartphones or other mobile devices. It becomes increasingly popular due to the recently explosive growth of smartphone users. Profile matching means two users comparing their personal profiles and is often the first step towards effective PMSN. It, however, conflicts with users' growing privacy concerns about disclosing their personal profiles to complete strangers before deciding to interact with them. This paper tackles this open challenge by designing a suite of novel fine-grained private matching protocols. Our protocols enable two users to perform profile matching without disclosing any information about their profiles beyond the comparison result. In contrast to existing coarse-grained private matching schemes for PMSN, our protocols allow finer differentiation between PMSN users and can support a wide range of matching metrics at different privacy levels. The security and communication/computation overhead of our protocols are thoroughly analyzed and evaluated via detailed simulations.

Fine-grained private matching for proximity-based mobile social networking

RFID (radio-frequency identification) is an emerging technology with extensive applications such as transportation and logistics, object tracking, and inventory management. How to quickly identify the missing RFID tags and thus their associated objects is a practically important problem in many large-scale RFID systems. This paper presents three novel methods to quickly identify the missing tags in a large-scale RFID system of thousands of tags. Our protocols can reduce the time for identifying all the missing tags by up to 75% in comparison to the state of art.

Fast identification of the missing tags in a large RFID system

Proximity-based mobile social networking (PMSN) refers to the social interaction among physically proximate mobile users. The first step toward effective PMSN is for mobile users to choose whom to interact with. Profile matching refers to two users comparing their personal profiles and is promising for user selection in PMSN. It, however, conflicts with users' growing privacy concerns about disclosing their personal profiles to complete strangers. This paper tackles this open challenge by designing novel fine-grained private matching protocols. Our protocols enable two users to perform profile matching without disclosing any information about their profiles beyond the comparison result. In contrast to existing coarse-grained private matching schemes for PMSN, our protocols allow finer differentiation between PMSN users and can support a wide range of matching metrics at different privacy levels. The performance of our protocols is thoroughly analyzed and evaluated via real smartphone experiments.

Privacy-Preserving Profile Matching for Proximity-Based Mobile Social Networking

The owner and users of a sensor network may be different, which necessitates privacy-preserving access control. On the one hand, the network owner need enforce strict access control so that the sensed data are only accessible to users willing to pay. On the other hand, users wish to protect their respective data access patterns whose disclosure may be used against their interests. This paper presents DP2AC, a Distributed Privacy-Preserving Access Control scheme for sensor networks, which is the first work of its kind. Users in DP2AC purchase tokens from the network owner whereby to query data from sensor nodes which will reply only after validating the tokens. The use of blind signatures in token generation ensures that tokens are publicly verifiable yet unlinkable to user identities, so privacy-preserving access control is achieved. A central component in DP2AC is to prevent malicious users from reusing tokens, for which we propose a suite of distributed token reuse detection (DTRD) schemes without involving the base station. These schemes share the essential idea that a sensor node checks with some other nodes (called witnesses) whether a token has been used, but they differ in how the witnesses are chosen. We thoroughly compare their performance with regard to TRD capability, communication overhead, storage overhead, and attack resilience. The efficacy and efficiency of DP2AC are confirmed by detailed performance evaluations.

Rui Zhang

Papers

PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems

Fine-grained private matching for proximity-based mobile social networking

Fast identification of the missing tags in a large RFID system

Privacy-Preserving Profile Matching for Proximity-Based Mobile Social Networking

Distributed Privacy-Preserving Access Control in Sensor Networks