Location Privacy Raja Khurram Shahzad 1984 "It was terribly - PowerPoint PPT Presentation

Location Privacy Raja Khurram Shahzad

1984 "It was terribly dangerous to let your thoughts wander when you were in any public place or within range of a telescreen. The smallest thing could give you away . A nervous tic, an unconscious look of anxiety, a habit of muttering to yourself--anything that carried with it the suggestion of abnormality, of having something to hide. In any case, to wear an improper expression on your face...; was itself a punishable offense. There was even a word for it in Newspeak: facecrime..." - George Orwell, 1984 , Book 1, Chapter 5

1984 vs Reality — 1984 : a novel envisioned a world — ”Everyone is being watched, practically at all times and places”. — Real world — Lifelog (dapra’s project) — Attest that continuously tracking where individuals go and what they do can be done with today’s technologies. — Many beneficial applications,i.e., Location based services (LBS) but personal privacy issues.

Reality — Location Based Services — Seamlessly and ubiquitously integrated into our lives. — Nexbus: provides location based transport data. — Cyberguide: context-aware location-based electronic guide assistantce in exploring physical spaces and cyberspaces. — Emergency: fcc requires wireless carriers to provide precise location information within 125m.

Location Privacy Risks — Deployment of LBS open doors for adversaries — To endanger the location privacy of mobile clients — To expose LBS to significant vulnerabilities for abuse — Space or Time correlated inference Attacks — Restricted Space Identification attack — Consider a mobile client which receives a real-time traffic and roadside information service from an LBS provider. If a user submits her service request messages with raw position information, the privacy of the user can be compromised.

Location Privacy Risks — LBS providers are not trusted but semi-honest. — Semihonest: the third-party LBS providers are honest and can correctly process and respond to messages, but are curious in that they may attempt to determine the identity of a user based on information received and information of physical world. — For instance, if the LBS provider has access to information that associates location with identity, such as person A lives in location L, and if it observes that all request messages within location L are from a single user, then it can infer that the identity of the user requesting the roadside information service is A. — Once the identity of the user is revealed, further tracking of future positions can be performed

Location Privacy Risks — Observation Identification — Reveal the user’s identity by relating some external observation on location-identity binding to a message. — For instance, if person A was reported to visit location L during time interval T, and if the LBS provider observed that all request messages during time interval T came from a single user within location L, then it can infer that the identity of the user in question is A.

Architecture of Service — In order to protect the location information from third parties that are semihonest but not completely trusted, we define a security perimeter around the mobile client. — Security Perimeter — The mobile client of the user — The trusted anonymity server — A secure channel where the communication between the two is secured through encryption

Architecture — The anonymity server is a secure gateway to the semihonest LBS providers for the mobile clients. — It runs a message perturbation engine, which performs location perturbation on the messages received from the mobile clients before forwarding them to the LBS provider. — The anonymity server upon receiving a message from a mobile client — Removes any identifiers such as internet protocol (ip) addresses — Perturbs the location information through spatio-temporal cloaking — Forwards the anonymized message to the LBS provider

Architecture — Spatial cloaking: replacing a 2D point location by a spatial range, where the original point location lies anywhere within the range. — Temporal cloaking: replacing a time point associated with the location point with a time interval that includes the original time point. — Location perturbation: the combination of spatial cloaking and temporal cloaking.

Architecture — Two approaches: — Policy-based: mobile clients specify their location privacy preferences as policies and completely trust that the third party LBS providers adhere to these policies. — Anonymity-based: the LBS providers are assumed to be semihonest instead of completely trusted. — Assumption: anonymous location-based applications do not require user identities for providing service.

Anonymity Approach: k -Anonymity — Originally introduced in the context of relational data privacy. — Addresses the question of “how a data holder can release its private data with guarantees that the individual subjects of the data cannot be identified whereas the data remain practically useful”. — Example: A medical institution release a table of medical records with the names of the individuals replaced with dummy identifiers. However, some set of attributes can still lead to identity breaches. Such as the combination of birth date, zip code, and gender attributes in the disclosed table can be joined with some publicly available information source like a voters list table

Anonymity Approach: k -Anonymity — k -anonymity prevents privacy breach — ensure that each individual record can only be released if there are at least k - 1 distinct individuals whose associated records are indistinguishable from the former. — In the context of LBSs and mobile clients, location k- anonymity refers to the k-anonymity usage of location information. — A subject is considered location k-anonymous if and only if the location information (Message) sent from a mobile client to an LBS is indistinguishable from the location information of at least k - 1 other mobile clients.

Anonymity Approach: Message Anonymization — Varying Location Privacy Requirement — Ensure different levels of service quality — Each mobile client specifies its anonymity level (k value), spatial tolerance, and temporal tolerance. — The main task of a location anonymity server is to transform each message received from mobile clients into a new message that can be safely ( k -anonymity) forwarded to the LBS provider

Anonymity Approach: Message Anonymization — The key idea that underlies the location k -anonymity model is twofold. — Spatial Cloaking: A given degree of location anonymity can be maintained, regardless of population density, by decreasing the location accuracy through enlarging the exposed spatial area such that there are other k - 1 mobile clients present in the same spatial area. — Temporal Cloaking: Location anonymity can be achieved by delaying the message until k mobile clients have visited the same area located by the message sender.

Anonymity Approach: Message Anonymization Notations Meanings Source Message Set s A message in set S m s Anonymity Level k Sender Id, u id , Message Number r no Temporal and Spatial Tolerance d t, d x, d y Spatio-temporal point of m s L(ms) = (x, y, t) Message contents C

Anonymity Approach: Message Anonymization — Set of messages received from the mobile clients as S. We formally define the messages in the set S as : — Messages are uniquely identifiable by the sender’s identifier, message reference number pairs ( u id , r no ), within the set S. — Messages from the same mobile client have the same sender identifiers but different reference numbers. — x, y, and t together form the 3D spatio-temporal location point of the message, denoted as L(m s ) .

Anonymity Approach: Message Anonymization — The coordinate ( x, y ) refers to the spatial position of the mobile client in the 2D space (x-axis and y-axis). — Time stamp t refers to the time point at which the mobile client was present at that position (temporal dimension: t- axis). — The k value of the message specifies the desired minimum anonymity level. — k =1, anonymity is not required — k >1 perturbed message will be assigned a spatio-temporal cloaking box

Anonymity Approach: Message Anonymization — dt, dx, dy : dependent on the requirements of the external LBS and user’s preferences with regard to QoS. — dt : represents the temporal tolerance specified by the user. — the perturbed message should have a spatio-temporal cloaking box whose projection on the temporal dimension does not contain any point more than dt distance away from t. — defines a deadline for the message such that a message should be anonymized until time — dx and dy specify the tolerances with respect to the spatial dimensions. — Larger spatial tolerances may result in less accurate results to location- dependent service requests, and larger temporal tolerances may result in higher latencies of the messages.